'74.0' '71.0' '71.9' '70.7' '68.5' '70.5' '69.0' '72.9' '73.8' '72.5'
 '70.9' '70.8' '71.1' '74.0' '73.8' '75.2' '76.7' '77.3' '77.5' '78.3'
 '78.3' '76.8' '76.0' '72.3' '79.9' '80.9' '80.6' '80.2' '85.0' '85.6'
 '85.6' '86.3' '86.4' '89.8' '89.9' '89.9' '86.4' '80.4' '83.4' '84.8'
 '92.8' '85.9' '84.5' '86.3' '89.4' '92.4' '92.2' '92.4' '90.9' '91.7'
 '88.9' '92.3' '92.6' '92.9' '92.8' '91.0' '93.0' '93.9' '94.3' '94.3'
 '94.3' '90.8' '89.4' '91.2' '91.2' '91.2' '90.3' '90.0' '90.0' '88.5'
 '82.9' '76.0' '76.5' '75.0' '79.1' '76.0' '84.8' '84.2' '84.2' '85.2'
 '85.2' '89.7' '91.1' '91.3' '91.0' '90.8' '91.2' '91.4' '90.0' '89.9'
 '89.9' '89.6' '91.7' '91.7' '91.7' '90.9' '90.4' '89.7' '90.3' '88.8'
 '88.8' '88.6' '90.6' '85.6' '86.1' '84.8']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['72.34' '65.34' '64.51' '67.33' '73.12' '75.61' '70.58' '73.83' '76.44'
 '73.73' '74.16' '72.75' '70.15' '73.55' '77.41' '77.95' '77.95' '77.95'
 '77.95' '73.18' '77.52' '75.89' '75.82' '74.55' '76.14' '77.42' '72.02'
 '68.7' '62.43' '76.97' '75.27' '73.91' '69.76' '71.36' '69.23' '66.94'
 '71.4' '73.11' '63.03' '59.84' '64.18' '64.61' '68.71' '73.02' '71.29'
 '70.13' '68.64' '73.21' '74.27' '73.72' '74.23' '74.23' '77.42' '73.21'
 '73.21' '72.91' '71.72' '73.12' '77.07' '76.57' '80.44' '77.25' '79.75'
 '77.62' '79.27' '76.73' '78.18' '79.22' '78.81' '80.5' '77.55' '77.55'
 '78.47' '81.31' '82.73' '78.67' '79.69' '82.33' '84.26' '81.63' '76.86'
 '80.6' '84.05' '82.23' '77.66' '67.91' '64.49' '77.15' '78.81' '79.52'
 '77.29' '77.29' '77.29' '83.45' '83.45' '78.98' '80.8' '85.27' '85.27'
 '85.27' '86.49' '82.02' '79.48' '84.66' '72.37' '72.21' '74.24' '71.91'
 '75.97' '69.88' '77.59' '75.06' '79.5' '80.85' '80.85' '78.41' '84.64'
 '86.7' '86.68' '86.68' '89.0' '93.45' '92.9' '92.9' '89.45' '94.65'
 '97.27' '87.73' '90.63' '90.63' '88.34' '88.67' '93.37' '90.75' '95.12'
 '94.24' '94.9' '81.97' '87.65' '92.32' '92.68' '96.87' '88.64' '88.64'
 '87.94' '91.26' '91.45' '87.75' '89.93' '93.63' '95.6' '96.3' '92.73'
 '88.24' '94.06' '88.1' '86.9' '86.6' '81.9' '88.24' '84.7' '84.9' '93.2'
 '95.4' '91.1' '93.3' '93.0' '80.4' '84.8' '85.6' '90.2' '86.6' '77.0'
 '80.9' '81.2' '84.7' '76.0' '69.7' '76.2' '86.4' '90.76' '90.9' '91.8'
 '92.7' '92.7' '90.5' '86.6' '86.6' '86.6' '83.4' '86.5' '89.9' '88.1'
 '88.8' '88.4' '87.2' '88.0' '86.7' '82.3' '88.9' '83.8' '84.8' '84.3'
 '89.2' '86.6' '88.2' '88.2' '87.0' '83.0' '87.6' '84.9' '80.1' '88.8'
 '90.2' '91.9' '91.9' '88.0' '84.8' '83.1' '82.9' '81.0' '88.4' '86.6'
 '84.1' '89.3' '85.6' '85.4' '85.6' '77.3' '82.9' '78.9' '79.2' '75.5'
 '76.3' '75.9' '75.9' '78.2' '78.2' '82.6' '82.9' '81.5' '79.3' '82.7'
 '83.1' '79.2' '78.9' '78.6' '78.9' '73.2' '71.3' '73.8' '77.5' '79.0'
 '80.1' '80.7' '73.5' '71.7' '73.0' '60.5' '72.2' '82.5' '82.7' '78.1'
 '78.1' '81.2' '82.8' '74.0' '73.3' '62.9' '78.8' '80.6' '77.4' '78.1'
 '78.1' '84.4' '77.3' '76.8' '77.4' '75.8' '73.0' '73.7' '71.9' '78.7'
 '72.4' '69.1' '72.1' '72.8' '73.7' '75.1' '75.3' '68.3' '66.7' '74.1'
 '76.6' '77.6' '78.5' '78.6' '72.3' '72.9' '69.1' '65.9' '67.6' '69.3'
 '68.2' '67.2' '70.6' '70.7' '73.5' '76.0' '77.0' '76.1' '77.6' '75.2'
 '71.2' '73.1' '72.5' '72.0' '66.9' '64.0' '63.7' '60.3' '63.6' '69.4'
 '65.9' '72.2' '77.0' '75.6' '75.9' '75.9' '73.2' '74.4' '71.5' '80.7'
 '80.1' '79.8' '81.8' '80.3' '80.9' '80.9' '80.6' '80.2' '79.7' '71.8'
 '72.7' '83.5' '77.5' '81.4' '80.9' '77.8' '75.0' '79.7' '79.3' '79.0'
 '79.2' '80.3' '82.2' '81.8' '83.5' '77.9' '80.1' '81.6' '75.9' '75.0'
 '77.0' '78.8' '75.4' '75.4' '74.6' '79.4' '75.5' '78.9' '85.3' '77.5'
 '82.0' '83.8' '83.8' '84.0' '79.0' '82.0' '82.4' '81.7' '82.0' '82.6'
 '84.1' '80.9' '78.1' '77.3' '74.4' '68.2' '77.4' '79.9' '82.2' '72.0'
 '71.5' '72.1' '73.6' '79.3' '76.6' '75.2' '79.7' '77.1' '79.4' '81.9'
 '79.6' '79.8' '79.7' '83.8' '82.6' '82.6' '82.2' '82.6' '79.4']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['72.34' '65.34' '64.51' '67.33' '73.12' '75.61' '70.58' '73.83' '76.44'
 '73.73' '74.16' '72.75' '70.15' '73.55' '77.41' '77.95' '77.95' '77.95'
 '77.95' '73.18' '77.52' '75.89' '75.82' '74.55' '76.14' '77.42' '72.02'
 '68.7' '62.43' '76.97' '75.27' '73.91' '69.76' '71.36' '69.23' '66.94'
 '71.4' '73.11' '63.03' '59.84' '64.18' '64.61' '68.71' '73.02' '71.29'
 '70.13' '68.64' '73.21' '74.27' '73.72' '74.23' '74.23' '77.42' '73.21'
 '73.21' '72.91' '71.72' '73.12' '77.07' '76.57' '80.44' '77.25' '79.75'
 '77.62' '79.27' '76.73' '78.18' '79.22' '78.81' '80.5' '77.55' '77.55'
 '78.47' '81.31' '82.73' '78.67' '79.69' '82.33' '84.26' '81.63' '76.86'
 '80.6' '84.05' '82.23' '77.66' '67.91' '64.49' '77.15' '78.81' '79.52'
 '77.29' '77.29' '77.29' '83.45' '83.45' '78.98' '80.8' '85.27' '85.27'
 '85.27' '86.49' '82.02' '79.48' '84.66' '72.37' '72.21' '74.24' '71.91'
 '75.97' '69.88' '77.59' '75.06' '79.5' '80.85' '80.85' '78.41' '84.64'
 '86.7' '86.68' '86.68' '89.0' '93.45' '92.9' '92.9' '89.45' '94.65'
 '97.27' '87.73' '90.63' '90.63' '88.34' '88.67' '93.37' '90.75' '95.12'
 '94.24' '94.9' '81.97' '87.65' '92.32' '92.68' '96.87' '88.64' '88.64'
 '87.94' '91.26' '91.45' '87.75' '89.93' '93.63' '95.6' '96.3' '92.73'
 '88.24' '94.06' '88.1' '86.9' '86.6' '81.9' '88.24' '84.7' '84.9' '93.2'
 '95.4' '91.1' '93.3' '93.0' '80.4' '84.8' '85.6' '90.2' '86.6' '77.0'
 '80.9' '81.2' '84.7' '76.0' '69.7' '76.2' '86.4' '90.76' '90.9' '91.8'
 '92.7' '92.7' '90.5' '86.6' '86.6' '86.6' '83.4' '86.5' '89.9' '88.1'
 '88.8' '88.4' '87.2' '88.0' '86.7' '82.3' '88.9' '83.8' '84.8' '84.3'
 '89.2' '86.6' '88.2' '88.2' '87.0' '83.0' '87.6' '84.9' '80.1' '88.8'
 '90.2' '91.9' '91.9' '88.0' '84.8' '83.1' '82.9' '81.0' '88.4' '86.6'
 '84.1' '89.3' '85.6' '85.4' '85.6' '77.3' '82.9' '78.9' '79.2' '75.5'
 '76.3' '75.9' '75.9' '78.2' '78.2' '82.6' '82.9' '81.5' '79.3' '82.7'
 '83.1' '79.2' '78.9' '78.6' '78.9' '73.2' '71.3' '73.8' '77.5' '79.0'
 '80.1' '80.7' '73.5' '71.7' '73.0' '60.5' '72.2' '82.5' '82.7' '78.1'
 '78.1' '81.2' '82.8' '74.0' '73.3' '62.9' '78.8' '80.6' '77.4' '78.1'
 '78.1' '84.4' '77.3' '76.8' '77.4' '75.8' '73.0' '73.7' '71.9' '78.7'
 '72.4' '69.1' '72.1' '72.8' '73.7' '75.1' '75.3' '68.3' '66.7' '74.1'
 '76.6' '77.6' '78.5' '78.6' '72.3' '72.9' '69.1' '65.9' '67.6' '69.3'
 '68.2' '67.2' '70.6' '70.7' '73.5' '76.0' '77.0' '76.1' '77.6' '75.2'
 '71.2' '73.1' '72.5' '72.0' '66.9' '64.0' '63.7' '60.3' '63.6' '69.4'
 '65.9' '72.2' '77.0' '75.6' '75.9' '75.9' '73.2' '74.4' '71.5' '80.7'
 '80.1' '79.8' '81.8' '80.3' '80.9' '80.9' '80.6' '80.2' '79.7' '71.8'
 '72.7' '83.5' '77.5' '81.4' '80.9' '77.8' '75.0' '79.7' '79.3' '79.0'
 '79.2' '80.3' '82.2' '81.8' '83.5' '77.9' '80.1' '81.6' '75.9' '75.0'
 '77.0' '78.8' '75.4' '75.4' '74.6' '79.4' '75.5' '78.9' '85.3' '77.5'
 '82.0' '83.8' '83.8' '84.0' '79.0' '82.0' '82.4' '81.7' '82.0' '82.6'
 '84.1' '80.9' '78.1' '77.3' '74.4' '68.2' '77.4' '79.9' '82.2' '72.0'
 '71.5' '72.1' '73.6' '79.3' '76.6' '75.2' '79.7' '77.1' '79.4' '81.9'
 '79.6' '79.8' '79.7' '83.8' '82.6' '82.6' '82.2' '82.6' '79.4']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['605.0' '603.0' '605.0' ... '800.0' '780.0' '785.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['605.0' '603.0' '605.0' ... '800.0' '780.0' '785.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['11250.0' '11250.0' '11250.0' ... '11330.0' '11300.0' '11300.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['11250.0' '11250.0' '11250.0' ... '11330.0' '11300.0' '11300.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5550.0' '5550.0' '5550.0' ... '6700.0' '6700.0' '6700.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5550.0' '5550.0' '5550.0' ... '6700.0' '6700.0' '6700.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['842.0' '841.0' '841.0' ... '1076.0' '1046.0' '1055.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['842.0' '841.0' '841.0' ... '1076.0' '1046.0' '1055.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['85.98' '90.3' '91.56' '95.84' '45.87' '90.98' '98.14' '104.06' '96.62'
 '93.38' '96.96' '89.67868' '91.84875' '99.89185' '101.6118' '99.85013'
 '89.17518' '69.79849' '105.144' '112.442' '106.838' '101.59' '102.8'
 '106.0' '97.43' '103.14' '98.824' '114.1201' '59.59233' '95.24' '97.889'
 '116.435' '103.092' '107.673' '100.147' '97.646' '101.551' '99.534'
 '110.061' '114.08435' '23.5414' '89.22314' '146.207' '206.487' '161.568'
 '159.769' '147.175' '131.521' '116.6' '120.396' '122.909' '130.76075'
 '90.58024' '96.735' '121.548' '121.234' '125.153' '116.982' '125.24'
 '124.092' '125.005' '132.448' '142.836' '166.06964' '81.34049' '118.253'
 '122.594' '140.282' '135.045' '135.839' '124.901' '120.68604' '113.688'
 '112.665' '110.029' '122.171' '69.474' '129.049' '127.41' '127.41'
 '120.218']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['85.98' '90.3' '91.56' '95.84' '45.87' '90.98' '98.14' '104.06' '96.62'
 '93.38' '96.96' '89.67868' '91.84875' '99.89185' '101.6118' '99.85013'
 '89.17518' '69.79849' '105.144' '112.442' '106.838' '101.59' '102.8'
 '106.0' '97.43' '103.14' '98.824' '114.1201' '59.59233' '95.24' '97.889'
 '116.435' '103.092' '107.673' '100.147' '97.646' '101.551' '99.534'
 '110.061' '114.08435' '23.5414' '89.22314' '146.207' '206.487' '161.568'
 '159.769' '147.175' '131.521' '116.6' '120.396' '122.909' '130.76075'
 '90.58024' '96.735' '121.548' '121.234' '125.153' '116.982' '125.24'
 '124.092' '125.005' '132.448' '142.836' '166.06964' '81.34049' '118.253'
 '122.594' '140.282' '135.045' '135.839' '124.901' '120.68604' '113.688'
 '112.665' '110.029' '122.171' '69.474' '129.049' '127.41' '127.41'
 '120.218']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['128.62' '125.93' '142.77' '143.2' '63.9' '109.2' '117.97' '130.0'
 '146.93' '161.48' '165.87' '147.7985' '124.9758' '131.2605' '138.5494'
 '131.2157' '114.2509' '71.84248' '109.715' '122.005' '147.936' '167.9'
 '167.6' '160.1' '135.13' '127.41' '132.348' '136.5076' '71.16318'
 '86.577' '96.711' '121.872' '143.373' '167.47' '156.989' '147.55'
 '127.129' '121.152' '139.478' '135.37291' '24.97743' '65.07991' '67.399'
 '89.057' '128.747' '153.175' '162.066' '152.252' '131.8' '125.5' '139.1'
 '135.41127' '105.13046' '92.528' '111.226' '121.97' '151.483' '165.75'
 '175.991' '167.226' '164.374' '154.138' '159.269' '158.06651' '85.8061'
 '102.241' '113.324' '151.992' '180.416' '196.378' '184.856' '159.81973'
 '136.547' '131.197' '142.938' '141.268' '75.511' '134.849' '129.164'
 '129.164' '132.984']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['128.62' '125.93' '142.77' '143.2' '63.9' '109.2' '117.97' '130.0'
 '146.93' '161.48' '165.87' '147.7985' '124.9758' '131.2605' '138.5494'
 '131.2157' '114.2509' '71.84248' '109.715' '122.005' '147.936' '167.9'
 '167.6' '160.1' '135.13' '127.41' '132.348' '136.5076' '71.16318'
 '86.577' '96.711' '121.872' '143.373' '167.47' '156.989' '147.55'
 '127.129' '121.152' '139.478' '135.37291' '24.97743' '65.07991' '67.399'
 '89.057' '128.747' '153.175' '162.066' '152.252' '131.8' '125.5' '139.1'
 '135.41127' '105.13046' '92.528' '111.226' '121.97' '151.483' '165.75'
 '175.991' '167.226' '164.374' '154.138' '159.269' '158.06651' '85.8061'
 '102.241' '113.324' '151.992' '180.416' '196.378' '184.856' '159.81973'
 '136.547' '131.197' '142.938' '141.268' '75.511' '134.849' '129.164'
 '129.164' '132.984']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['74.0' '74.0' '74.0' '75.0' '75.0' '75.0' '75.0' '75.0' '75.0' '75.0'
 '75.0' '75.0' '75.0' '75.0' '74.5' '74.0' '54.0' '18.0' '72.0' '78.0'
 '79.0' '79.0' '79.0' '79.0' '77.0' '74.0' '74.0' '74.0' '74.0' '74.0'
 '74.0' '74.0' '74.0' '74.0' '73.5' '72.5' '72.0' '72.0' '72.0' '71.0'
 '71.0' '70.5' '70.5' '70.5' '70.5' '70.5' '70.5' '70.5' '71.5' '71.5'
 '72.0' '73.0' '75.0' '75.0' '76.0' '77.0' '78.5' '79.5' '81.0' '81.0'
 '81.0' '81.0' '81.0' '81.0' '81.0' '80.6' '78.0' '76.0' '65.0' '35.0'
 '17.0' '49.0' '75.0' '81.0' '81.0' '81.0' '81.0' '81.0' '81.0' '81.0'
 '81.0' '81.0' '81.0' '81.0' '81.0' '81.0' '81.0' '81.0' '80.5' '79.5'
 '76.0' '75.5' '75.5' '75.5' '75.5' '75.5' '76.5' '76.5' '76.5' '77.0'
 '77.0' '77.0' '77.6' '77.6' '77.6' '77.8' '77.8' '77.8' '77.5' '77.4'
 '77.4' '77.4' '77.4' '77.0' '76.0' '73.9' '72.5' '54.0' '17.0' '51.0'
 '65.0' '77.2' '77.5' '78.2' '80.0' '80.0' '80.0' '80.3' '80.3' '80.3'
 '79.9' '79.8' '78.9' '78.6' '78.5' '78.0' '77.4' '76.4' '76.8' '76.8'
 '76.8' '76.3' '71.5' '69.0' '69.0' '69.0' '69.0' '69.5' '69.5' '69.5'
 '71.0' '68.5' '68.0' '75.5' '79.5' '79.5' '79.5' '79.5' '79.5' '79.5'
 '79.5' '79.5' '79.5' '79.5' '79.2' '74.0' '69.0' '45.0' '20.0' '30.0'
 '58.0' '58.0' '68.0' '71.0' '73.0' '75.0' '75.0' '75.0' '73.0' '72.0'
 '72.0' '71.5' '73.0' '73.0' '73.0' '73.0' '72.0' '74.0' '74.0' '74.0'
 '73.0' '72.0' '72.0' '72.0' '72.0' '73.0' '73.0' '72.8' '74.0' '74.0'
 '75.0' '75.0' '76.0' '76.0' '78.0' '80.0' '80.0' '80.0' '80.0' '80.0'
 '80.0' '80.0' '80.0' '79.8' '79.8' '80.0' '78.0' '72.0' '63.0' '59.0'
 '52.0' '15.0' '50.0' '68.0' '75.0' '75.0' '76.0' '77.0' '77.0' '77.0'
 '77.0' '77.0' '77.0' '77.0' '76.0' '75.0' '74.0' '74.0' '74.0' '73.0'
 '72.0' '73.0' '73.0' '73.0' '72.0' '71.0' '71.0' '71.0' '71.0' '72.0'
 '73.0' '72.0' '70.0' '66.0' '64.0' '55.0' '64.0' '68.5' '70.0' '66.0'
 '68.0' '74.0' '76.0' '76.0' '76.0' '77.0' '77.0' '77.0' '74.0' '64.0'
 '58.0' '18.0' '17.0' '68.0' '68.0' '71.0' '72.0' '75.0' '79.0' '79.0'
 '78.3' '78.3' '78.3' '75.0' '75.7' '75.7' '75.0' '77.0' '79.0' '77.0'
 '65.0' '67.0' '64.0' '62.0' '60.0' '58.5' '57.5' '56.5' '54.0' '54.0'
 '54.0' '53.0' '55.0' '60.0' '63.0' '67.0' '68.0' '68.0' '65.0' '67.0'
 '65.0' '63.5' '63.0' '60.0' '60.0' '56.0' '56.0' '58.0' '56.5' '45.0'
 '28.0' '28.0' '24.0' '13.0' '3.0' '24.0' '46.0' '55.0' '65.0' '69.0'
 '71.0' '73.0' '73.0' '72.0' '71.5' '71.5' '71.0' '70.8' '70.8' '69.0'
 '67.0' '67.0' '66.0' '65.5' '65.0' '62.0' '61.0' '47.0' '61.0' '60.5'
 '60.5' '60.5' '61.0' '61.0' '62.0' '63.5' '65.5' '67.0' '66.0' '66.0'
 '66.0' '56.0' '61.0' '66.0' '67.0' '68.0' '68.0' '67.5' '66.0' '66.0'
 '65.0' '63.5' '63.5' '62.5' '62.5' '62.3' '61.0' '57.3' '51.0' '15.0'
 '0.0' '7.0' '45.0' '58.0' '64.5' '71.0' '71.0' '71.0' '73.0' '73.0'
 '73.0' '73.0' '73.0' '73.3' '73.3' '73.3' '73.3' '73.3' '73.0' '73.0'
 '72.8' '72.2' '72.0' '71.0' '67.7' '64.5' '63.7' '63.4' '63.4' '64.0'
 '66.0' '66.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['74.0' '74.0' '74.0' '75.0' '75.0' '75.0' '75.0' '75.0' '75.0' '75.0'
 '75.0' '75.0' '75.0' '75.0' '74.5' '74.0' '54.0' '18.0' '72.0' '78.0'
 '79.0' '79.0' '79.0' '79.0' '77.0' '74.0' '74.0' '74.0' '74.0' '74.0'
 '74.0' '74.0' '74.0' '74.0' '73.5' '72.5' '72.0' '72.0' '72.0' '71.0'
 '71.0' '70.5' '70.5' '70.5' '70.5' '70.5' '70.5' '70.5' '71.5' '71.5'
 '72.0' '73.0' '75.0' '75.0' '76.0' '77.0' '78.5' '79.5' '81.0' '81.0'
 '81.0' '81.0' '81.0' '81.0' '81.0' '80.6' '78.0' '76.0' '65.0' '35.0'
 '17.0' '49.0' '75.0' '81.0' '81.0' '81.0' '81.0' '81.0' '81.0' '81.0'
 '81.0' '81.0' '81.0' '81.0' '81.0' '81.0' '81.0' '81.0' '80.5' '79.5'
 '76.0' '75.5' '75.5' '75.5' '75.5' '75.5' '76.5' '76.5' '76.5' '77.0'
 '77.0' '77.0' '77.6' '77.6' '77.6' '77.8' '77.8' '77.8' '77.5' '77.4'
 '77.4' '77.4' '77.4' '77.0' '76.0' '73.9' '72.5' '54.0' '17.0' '51.0'
 '65.0' '77.2' '77.5' '78.2' '80.0' '80.0' '80.0' '80.3' '80.3' '80.3'
 '79.9' '79.8' '78.9' '78.6' '78.5' '78.0' '77.4' '76.4' '76.8' '76.8'
 '76.8' '76.3' '71.5' '69.0' '69.0' '69.0' '69.0' '69.5' '69.5' '69.5'
 '71.0' '68.5' '68.0' '75.5' '79.5' '79.5' '79.5' '79.5' '79.5' '79.5'
 '79.5' '79.5' '79.5' '79.5' '79.2' '74.0' '69.0' '45.0' '20.0' '30.0'
 '58.0' '58.0' '68.0' '71.0' '73.0' '75.0' '75.0' '75.0' '73.0' '72.0'
 '72.0' '71.5' '73.0' '73.0' '73.0' '73.0' '72.0' '74.0' '74.0' '74.0'
 '73.0' '72.0' '72.0' '72.0' '72.0' '73.0' '73.0' '72.8' '74.0' '74.0'
 '75.0' '75.0' '76.0' '76.0' '78.0' '80.0' '80.0' '80.0' '80.0' '80.0'
 '80.0' '80.0' '80.0' '79.8' '79.8' '80.0' '78.0' '72.0' '63.0' '59.0'
 '52.0' '15.0' '50.0' '68.0' '75.0' '75.0' '76.0' '77.0' '77.0' '77.0'
 '77.0' '77.0' '77.0' '77.0' '76.0' '75.0' '74.0' '74.0' '74.0' '73.0'
 '72.0' '73.0' '73.0' '73.0' '72.0' '71.0' '71.0' '71.0' '71.0' '72.0'
 '73.0' '72.0' '70.0' '66.0' '64.0' '55.0' '64.0' '68.5' '70.0' '66.0'
 '68.0' '74.0' '76.0' '76.0' '76.0' '77.0' '77.0' '77.0' '74.0' '64.0'
 '58.0' '18.0' '17.0' '68.0' '68.0' '71.0' '72.0' '75.0' '79.0' '79.0'
 '78.3' '78.3' '78.3' '75.0' '75.7' '75.7' '75.0' '77.0' '79.0' '77.0'
 '65.0' '67.0' '64.0' '62.0' '60.0' '58.5' '57.5' '56.5' '54.0' '54.0'
 '54.0' '53.0' '55.0' '60.0' '63.0' '67.0' '68.0' '68.0' '65.0' '67.0'
 '65.0' '63.5' '63.0' '60.0' '60.0' '56.0' '56.0' '58.0' '56.5' '45.0'
 '28.0' '28.0' '24.0' '13.0' '3.0' '24.0' '46.0' '55.0' '65.0' '69.0'
 '71.0' '73.0' '73.0' '72.0' '71.5' '71.5' '71.0' '70.8' '70.8' '69.0'
 '67.0' '67.0' '66.0' '65.5' '65.0' '62.0' '61.0' '47.0' '61.0' '60.5'
 '60.5' '60.5' '61.0' '61.0' '62.0' '63.5' '65.5' '67.0' '66.0' '66.0'
 '66.0' '56.0' '61.0' '66.0' '67.0' '68.0' '68.0' '67.5' '66.0' '66.0'
 '65.0' '63.5' '63.5' '62.5' '62.5' '62.3' '61.0' '57.3' '51.0' '15.0'
 '0.0' '7.0' '45.0' '58.0' '64.5' '71.0' '71.0' '71.0' '73.0' '73.0'
 '73.0' '73.0' '73.0' '73.3' '73.3' '73.3' '73.3' '73.3' '73.0' '73.0'
 '72.8' '72.2' '72.0' '71.0' '67.7' '64.5' '63.7' '63.4' '63.4' '64.0'
 '66.0' '66.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['17250.0' '17250.0' '17260.0' ... '16950.0' '16950.0' '16950.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['17250.0' '17250.0' '17260.0' ... '16950.0' '16950.0' '16950.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['80.3' '81.2' '82.1' '82.1' '82.3' '83.2' '82.7' '82.9' '82.0' '81.5'
 '81.8' '81.3' '81.5' '82.8' '86.1' '85.1' '80.6' '76.1' '75.0' '78.3'
 '83.0' '85.9' '86.6' '86.2' '86.3' '87.3' '86.6' '86.4' '87.6' '87.5'
 '87.6' '87.3' '86.1' '86.6' '87.0' '88.1' '87.6' '87.8' '88.8' '88.3'
 '88.3' '89.1' '89.3' '89.2' '88.9' '88.8' '88.6' '88.7' '88.3' '88.0'
 '89.6' '91.5' '93.1' '94.4' '93.2' '93.1' '92.3' '92.1' '92.5' '93.1'
 '92.2' '91.2' '89.5' '89.2' '89.9' '91.2' '89.8' '88.6' '86.3' '86.3'
 '80.4' '81.0' '86.6' '89.9' '92.2' '93.4' '93.1' '93.0' '95.5' '97.3'
 '97.5' '96.9' '97.5' '97.7' '96.9' '96.3' '96.4' '96.8' '95.0' '92.2'
 '92.6' '93.9' '94.7' '96.0' '94.9' '92.2' '93.0' '91.2' '90.3' '90.7'
 '86.1' '80.4' '78.9' '82.9' '85.4' '88.4' '89.2' '88.7' '86.1' '87.8'
 '88.9' '88.8' '89.1' '88.0' '86.2' '84.8' '83.9' '81.9' '78.3' '73.3'
 '78.6' '85.4' '86.6' '88.5' '90.5' '90.5' '92.9' '93.2' '92.3' '92.8'
 '93.2' '93.8' '91.5' '89.9' '87.9' '88.5' '89.5' '91.1' '91.5' '92.9'
 '93.2' '91.1' '88.3' '86.7' '86.3' '87.7' '88.9' '90.1' '92.2' '92.0'
 '91.8' '92.1' '92.8' '92.8' '92.4' '91.6' '90.7' '88.9' '89.7' '90.3'
 '90.5' '89.3' '89.1' '87.3' '85.6' '84.9' '83.7' '79.6' '77.7' '75.4'
 '61.1' '59.5' '60.9' '64.1' '72.6' '80.0' '81.0' '83.9' '83.6' '84.0'
 '87.8' '87.0' '87.5' '87.4' '89.4' '89.3' '90.0' '90.6' '91.7' '90.7'
 '91.3' '92.0' '92.0' '90.9' '91.5' '91.0' '90.1' '90.1' '90.9' '91.5'
 '92.3' '92.7' '92.0' '91.8' '91.4' '90.6' '90.3' '90.9' '90.9' '90.7'
 '90.4' '92.1' '91.9' '91.4' '91.0' '90.7' '89.6' '89.1' '91.7' '88.7'
 '88.0' '82.5' '82.2' '82.3' '84.5' '88.6' '91.5' '93.9' '94.2' '92.8'
 '93.7' '93.6' '94.2' '93.7' '94.5' '93.6' '92.6' '90.9' '92.5' '90.8'
 '91.1' '93.4' '92.8' '93.1' '93.7' '93.5' '92.5' '91.5' '91.2' '89.9'
 '86.5' '87.7' '86.6' '87.7' '86.0' '83.6' '81.1' '81.6' '81.7' '82.9'
 '84.1' '84.6' '85.4' '87.8' '87.7' '85.0' '84.9' '83.7' '84.7' '84.0'
 '85.5' '87.7' '85.8' '83.4' '85.2' '89.8' '91.9' '92.9' '93.3' '94.0'
 '93.1' '91.8' '88.0' '79.6' '79.9' '79.3' '80.5' '81.2' '81.9' '83.1'
 '84.0' '85.0' '84.0' '84.4' '81.9' '79.5' '76.8' '78.0' '79.4' '81.1'
 '82.3' '81.2' '82.0' '84.0' '83.3' '84.3' '83.9' '83.4' '82.9' '83.6'
 '84.1' '84.1' '82.0' '79.5' '77.6' '75.3' '71.6' '69.2' '67.9' '66.8'
 '66.0' '67.5' '66.1' '63.6' '63.9' '64.1' '67.0' '78.0' '83.0' '86.1'
 '87.4' '88.5' '89.7' '90.8' '90.4' '88.9' '87.8' '87.2' '86.1' '84.7'
 '87.7' '88.9' '90.1' '92.3' '92.8' '92.8' '92.6' '93.1' '93.5' '94.0'
 '93.0' '93.5' '92.5' '92.3' '92.8' '92.8' '92.7' '92.9' '92.5' '88.5'
 '88.6' '89.1' '89.4' '90.6' '90.7' '89.6' '89.6' '89.4' '89.8' '90.9'
 '90.8' '89.9' '89.8' '89.0' '90.2' '90.4' '88.9' '84.6' '82.8' '79.0'
 '80.1' '83.6' '87.4' '89.0' '90.1' '90.7' '91.2' '92.2' '92.8' '93.1'
 '92.8' '93.1' '89.3' '88.5' '88.9' '89.0' '89.6' '89.3' '90.0' '88.2'
 '87.7' '86.2' '85.8' '86.4' '85.7' '87.0' '86.2' '86.7']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['80.3' '81.2' '82.1' '82.1' '82.3' '83.2' '82.7' '82.9' '82.0' '81.5'
 '81.8' '81.3' '81.5' '82.8' '86.1' '85.1' '80.6' '76.1' '75.0' '78.3'
 '83.0' '85.9' '86.6' '86.2' '86.3' '87.3' '86.6' '86.4' '87.6' '87.5'
 '87.6' '87.3' '86.1' '86.6' '87.0' '88.1' '87.6' '87.8' '88.8' '88.3'
 '88.3' '89.1' '89.3' '89.2' '88.9' '88.8' '88.6' '88.7' '88.3' '88.0'
 '89.6' '91.5' '93.1' '94.4' '93.2' '93.1' '92.3' '92.1' '92.5' '93.1'
 '92.2' '91.2' '89.5' '89.2' '89.9' '91.2' '89.8' '88.6' '86.3' '86.3'
 '80.4' '81.0' '86.6' '89.9' '92.2' '93.4' '93.1' '93.0' '95.5' '97.3'
 '97.5' '96.9' '97.5' '97.7' '96.9' '96.3' '96.4' '96.8' '95.0' '92.2'
 '92.6' '93.9' '94.7' '96.0' '94.9' '92.2' '93.0' '91.2' '90.3' '90.7'
 '86.1' '80.4' '78.9' '82.9' '85.4' '88.4' '89.2' '88.7' '86.1' '87.8'
 '88.9' '88.8' '89.1' '88.0' '86.2' '84.8' '83.9' '81.9' '78.3' '73.3'
 '78.6' '85.4' '86.6' '88.5' '90.5' '90.5' '92.9' '93.2' '92.3' '92.8'
 '93.2' '93.8' '91.5' '89.9' '87.9' '88.5' '89.5' '91.1' '91.5' '92.9'
 '93.2' '91.1' '88.3' '86.7' '86.3' '87.7' '88.9' '90.1' '92.2' '92.0'
 '91.8' '92.1' '92.8' '92.8' '92.4' '91.6' '90.7' '88.9' '89.7' '90.3'
 '90.5' '89.3' '89.1' '87.3' '85.6' '84.9' '83.7' '79.6' '77.7' '75.4'
 '61.1' '59.5' '60.9' '64.1' '72.6' '80.0' '81.0' '83.9' '83.6' '84.0'
 '87.8' '87.0' '87.5' '87.4' '89.4' '89.3' '90.0' '90.6' '91.7' '90.7'
 '91.3' '92.0' '92.0' '90.9' '91.5' '91.0' '90.1' '90.1' '90.9' '91.5'
 '92.3' '92.7' '92.0' '91.8' '91.4' '90.6' '90.3' '90.9' '90.9' '90.7'
 '90.4' '92.1' '91.9' '91.4' '91.0' '90.7' '89.6' '89.1' '91.7' '88.7'
 '88.0' '82.5' '82.2' '82.3' '84.5' '88.6' '91.5' '93.9' '94.2' '92.8'
 '93.7' '93.6' '94.2' '93.7' '94.5' '93.6' '92.6' '90.9' '92.5' '90.8'
 '91.1' '93.4' '92.8' '93.1' '93.7' '93.5' '92.5' '91.5' '91.2' '89.9'
 '86.5' '87.7' '86.6' '87.7' '86.0' '83.6' '81.1' '81.6' '81.7' '82.9'
 '84.1' '84.6' '85.4' '87.8' '87.7' '85.0' '84.9' '83.7' '84.7' '84.0'
 '85.5' '87.7' '85.8' '83.4' '85.2' '89.8' '91.9' '92.9' '93.3' '94.0'
 '93.1' '91.8' '88.0' '79.6' '79.9' '79.3' '80.5' '81.2' '81.9' '83.1'
 '84.0' '85.0' '84.0' '84.4' '81.9' '79.5' '76.8' '78.0' '79.4' '81.1'
 '82.3' '81.2' '82.0' '84.0' '83.3' '84.3' '83.9' '83.4' '82.9' '83.6'
 '84.1' '84.1' '82.0' '79.5' '77.6' '75.3' '71.6' '69.2' '67.9' '66.8'
 '66.0' '67.5' '66.1' '63.6' '63.9' '64.1' '67.0' '78.0' '83.0' '86.1'
 '87.4' '88.5' '89.7' '90.8' '90.4' '88.9' '87.8' '87.2' '86.1' '84.7'
 '87.7' '88.9' '90.1' '92.3' '92.8' '92.8' '92.6' '93.1' '93.5' '94.0'
 '93.0' '93.5' '92.5' '92.3' '92.8' '92.8' '92.7' '92.9' '92.5' '88.5'
 '88.6' '89.1' '89.4' '90.6' '90.7' '89.6' '89.6' '89.4' '89.8' '90.9'
 '90.8' '89.9' '89.8' '89.0' '90.2' '90.4' '88.9' '84.6' '82.8' '79.0'
 '80.1' '83.6' '87.4' '89.0' '90.1' '90.7' '91.2' '92.2' '92.8' '93.1'
 '92.8' '93.1' '89.3' '88.5' '88.9' '89.0' '89.6' '89.3' '90.0' '88.2'
 '87.7' '86.2' '85.8' '86.4' '85.7' '87.0' '86.2' '86.7']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['16.4' '15.3' '14.0' '11.6' '11.5' '10.8' '8.9' '9.1' '8.9' '8.8' '8.2'
 '7.3' '8.8' '9.9' '12.6' '15.3' '17.3' '21.1' '27.8' '29.0' '29.1' '29.7'
 '30.1' '30.7' '30.2' '30.4' '30.4' '28.8' '25.8' '25.8' '25.3' '27.2'
 '26.9' '24.2' '23.8' '24.5' '24.5' '24.3' '23.6' '22.8' '20.5' '19.0'
 '19.9' '21.0' '23.5' '22.8' '19.0' '18.0' '16.8' '14.0' '13.7' '13.9'
 '13.6' '16.2' '14.5' '13.5' '13.4' '11.4' '12.0' '13.2' '13.7' '14.2'
 '16.2' '16.0' '16.1' '17.1' '17.0' '17.9' '20.4' '22.0' '23.5' '24.5'
 '33.0' '32.7' '32.4' '31.7' '29.2' '27.0' '27.9' '23.8' '21.6' '22.3'
 '22.4' '20.5' '20.4' '21.0' '21.1' '21.0' '19.7' '20.7' '19.2' '20.2'
 '19.5' '18.6' '17.3' '14.4' '12.8' '11.4' '12.4' '12.7' '13.9' '17.3'
 '19.6' '21.0' '18.5' '19.8' '23.0' '22.1' '22.9' '21.2' '21.2' '22.7'
 '16.6' '15.5' '17.4' '16.1' '16.7' '13.5' '13.8' '13.8' '18.0' '27.2'
 '27.8' '28.6' '22.9' '24.2' '23.3' '22.5' '23.5' '22.4' '22.9' '24.0'
 '26.5' '26.9' '26.7' '26.2' '20.8' '21.2' '20.5' '15.0' '12.8' '13.1'
 '15.9' '17.9' '18.5' '16.6' '17.5' '12.2' '14.4' '14.8' '13.4' '14.3'
 '13.8' '14.2' '14.3' '16.5' '17.7' '16.7' '16.8' '16.3' '14.7' '12.8'
 '14.2' '14.2' '12.7' '12.0' '12.5' '13.5' '12.9' '15.7' '18.2' '30.4'
 '33.5' '35.7' '36.0' '31.7' '32.1' '32.8' '33.2' '26.6' '22.8' '25.1'
 '25.9' '23.7' '23.1' '23.4' '21.7' '20.9' '21.1' '21.7' '24.2' '26.3'
 '27.8' '29.6' '31.6' '29.4' '28.6' '27.5' '29.4' '29.8' '30.6' '30.0'
 '29.9' '28.7' '26.9' '24.5' '21.0' '11.6' '12.4' '14.2' '15.8' '14.7'
 '15.1' '13.4' '13.6' '13.2' '12.5' '11.7' '10.8' '9.7' '10.0' '13.0'
 '16.1' '17.6' '20.3' '23.4' '20.8' '20.8' '21.4' '22.5' '25.1' '25.5'
 '26.5' '26.7' '26.6' '26.4' '28.4' '27.9' '25.5' '25.4' '25.9' '22.4'
 '21.6' '19.1' '18.8' '18.0' '19.8' '18.6' '18.6' '21.1' '23.9' '25.3'
 '27.1' '26.1' '24.5' '23.2' '23.4' '16.9' '13.2' '10.3' '8.3' '12.9'
 '15.3' '18.6' '20.1' '19.5' '22.4' '22.0' '21.0' '21.0' '16.4' '15.0'
 '16.9' '19.8' '22.0' '29.4' '31.7' '30.0' '26.6' '26.8' '28.1' '31.6'
 '32.0' '36.0' '36.6' '36.4' '33.1' '32.5' '33.0' '35.7' '35.6' '34.0'
 '32.9' '36.1' '38.6' '37.5' '35.7' '36.1' '33.5' '32.1' '33.4' '32.2'
 '33.9' '32.2' '31.4' '30.4' '30.7' '32.8' '31.8' '31.8' '31.9' '31.5'
 '32.1' '30.8' '30.7' '30.6' '30.3' '28.3' '24.3' '18.0' '18.8' '21.2'
 '22.2' '20.2' '22.2' '24.8' '25.8' '27.1' '28.1' '26.8' '25.8' '25.2'
 '25.5' '24.2' '25.2' '26.5' '27.4' '29.3' '29.5' '30.9' '30.3' '30.7'
 '29.5' '28.1' '29.5' '28.5' '31.3' '30.9' '29.3' '30.2' '30.6' '30.9'
 '32.0' '32.6' '32.9' '31.9' '31.8' '28.1' '25.9' '26.9' '26.3' '30.1'
 '29.3' '29.4' '29.7' '27.2' '27.7' '25.5' '24.8' '23.4' '23.7' '22.6'
 '22.0' '22.5' '23.3' '23.2' '23.4' '20.3' '21.5' '23.9' '30.7' '32.0'
 '32.1' '32.0' '31.3' '31.2' '29.7' '29.8' '30.6' '30.7' '30.2' '29.4'
 '30.7' '30.5' '27.7' '27.2' '28.1' '29.1' '28.5' '27.3' '27.6' '29.4'
 '30.8' '29.6' '30.6' '32.4' '28.1' '29.5']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['16.4' '15.3' '14.0' '11.6' '11.5' '10.8' '8.9' '9.1' '8.9' '8.8' '8.2'
 '7.3' '8.8' '9.9' '12.6' '15.3' '17.3' '21.1' '27.8' '29.0' '29.1' '29.7'
 '30.1' '30.7' '30.2' '30.4' '30.4' '28.8' '25.8' '25.8' '25.3' '27.2'
 '26.9' '24.2' '23.8' '24.5' '24.5' '24.3' '23.6' '22.8' '20.5' '19.0'
 '19.9' '21.0' '23.5' '22.8' '19.0' '18.0' '16.8' '14.0' '13.7' '13.9'
 '13.6' '16.2' '14.5' '13.5' '13.4' '11.4' '12.0' '13.2' '13.7' '14.2'
 '16.2' '16.0' '16.1' '17.1' '17.0' '17.9' '20.4' '22.0' '23.5' '24.5'
 '33.0' '32.7' '32.4' '31.7' '29.2' '27.0' '27.9' '23.8' '21.6' '22.3'
 '22.4' '20.5' '20.4' '21.0' '21.1' '21.0' '19.7' '20.7' '19.2' '20.2'
 '19.5' '18.6' '17.3' '14.4' '12.8' '11.4' '12.4' '12.7' '13.9' '17.3'
 '19.6' '21.0' '18.5' '19.8' '23.0' '22.1' '22.9' '21.2' '21.2' '22.7'
 '16.6' '15.5' '17.4' '16.1' '16.7' '13.5' '13.8' '13.8' '18.0' '27.2'
 '27.8' '28.6' '22.9' '24.2' '23.3' '22.5' '23.5' '22.4' '22.9' '24.0'
 '26.5' '26.9' '26.7' '26.2' '20.8' '21.2' '20.5' '15.0' '12.8' '13.1'
 '15.9' '17.9' '18.5' '16.6' '17.5' '12.2' '14.4' '14.8' '13.4' '14.3'
 '13.8' '14.2' '14.3' '16.5' '17.7' '16.7' '16.8' '16.3' '14.7' '12.8'
 '14.2' '14.2' '12.7' '12.0' '12.5' '13.5' '12.9' '15.7' '18.2' '30.4'
 '33.5' '35.7' '36.0' '31.7' '32.1' '32.8' '33.2' '26.6' '22.8' '25.1'
 '25.9' '23.7' '23.1' '23.4' '21.7' '20.9' '21.1' '21.7' '24.2' '26.3'
 '27.8' '29.6' '31.6' '29.4' '28.6' '27.5' '29.4' '29.8' '30.6' '30.0'
 '29.9' '28.7' '26.9' '24.5' '21.0' '11.6' '12.4' '14.2' '15.8' '14.7'
 '15.1' '13.4' '13.6' '13.2' '12.5' '11.7' '10.8' '9.7' '10.0' '13.0'
 '16.1' '17.6' '20.3' '23.4' '20.8' '20.8' '21.4' '22.5' '25.1' '25.5'
 '26.5' '26.7' '26.6' '26.4' '28.4' '27.9' '25.5' '25.4' '25.9' '22.4'
 '21.6' '19.1' '18.8' '18.0' '19.8' '18.6' '18.6' '21.1' '23.9' '25.3'
 '27.1' '26.1' '24.5' '23.2' '23.4' '16.9' '13.2' '10.3' '8.3' '12.9'
 '15.3' '18.6' '20.1' '19.5' '22.4' '22.0' '21.0' '21.0' '16.4' '15.0'
 '16.9' '19.8' '22.0' '29.4' '31.7' '30.0' '26.6' '26.8' '28.1' '31.6'
 '32.0' '36.0' '36.6' '36.4' '33.1' '32.5' '33.0' '35.7' '35.6' '34.0'
 '32.9' '36.1' '38.6' '37.5' '35.7' '36.1' '33.5' '32.1' '33.4' '32.2'
 '33.9' '32.2' '31.4' '30.4' '30.7' '32.8' '31.8' '31.8' '31.9' '31.5'
 '32.1' '30.8' '30.7' '30.6' '30.3' '28.3' '24.3' '18.0' '18.8' '21.2'
 '22.2' '20.2' '22.2' '24.8' '25.8' '27.1' '28.1' '26.8' '25.8' '25.2'
 '25.5' '24.2' '25.2' '26.5' '27.4' '29.3' '29.5' '30.9' '30.3' '30.7'
 '29.5' '28.1' '29.5' '28.5' '31.3' '30.9' '29.3' '30.2' '30.6' '30.9'
 '32.0' '32.6' '32.9' '31.9' '31.8' '28.1' '25.9' '26.9' '26.3' '30.1'
 '29.3' '29.4' '29.7' '27.2' '27.7' '25.5' '24.8' '23.4' '23.7' '22.6'
 '22.0' '22.5' '23.3' '23.2' '23.4' '20.3' '21.5' '23.9' '30.7' '32.0'
 '32.1' '32.0' '31.3' '31.2' '29.7' '29.8' '30.6' '30.7' '30.2' '29.4'
 '30.7' '30.5' '27.7' '27.2' '28.1' '29.1' '28.5' '27.3' '27.6' '29.4'
 '30.8' '29.6' '30.6' '32.4' '28.1' '29.5']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['9.9' '9.2' '8.9' '7.2' '7.2' '8.1' '5.8' '7.5' '7.3' '6.0' '3.7' '2.5'
 '3.5' '5.7' '9.0' '12.1' '12.0' '14.0' '19.0' '18.2' '19.7' '21.7' '23.5'
 '25.6' '24.0' '24.4' '23.0' '18.2' '16.2' '16.4' '17.2' '19.0' '17.0'
 '13.1' '11.4' '11.8' '10.9' '7.9' '6.6' '6.8' '5.7' '6.5' '7.9' '10.3'
 '12.3' '10.3' '8.4' '6.8' '5.2' '4.5' '6.4' '8.5' '8.2' '11.1' '7.4'
 '6.6' '5.4' '4.5' '5.1' '7.8' '8.2' '9.3' '10.8' '8.6' '8.7' '7.4' '8.0'
 '10.5' '9.3' '9.8' '7.0' '8.2' '14.0' '15.1' '18.5' '18.3' '13.0' '13.7'
 '13.2' '10.0' '7.7' '9.6' '12.7' '9.8' '11.9' '13.8' '12.6' '13.5' '11.1'
 '13.3' '10.6' '12.0' '10.0' '10.3' '10.1' '7.6' '9.2' '8.5' '9.6' '9.5'
 '12.0' '16.2' '20.0' '21.2' '17.1' '19.2' '21.6' '20.5' '21.7' '18.7'
 '19.7' '20.2' '14.4' '13.0' '15.0' '14.7' '15.6' '12.5' '11.1' '9.2'
 '11.1' '14.2' '16.8' '18.0' '11.5' '14.2' '13.4' '13.3' '15.3' '12.5'
 '15.1' '16.0' '17.2' '18.3' '19.6' '20.4' '12.2' '13.6' '12.0' '7.1'
 '6.5' '8.3' '11.0' '13.4' '14.4' '13.2' '12.4' '7.9' '9.5' '11.2' '10.0'
 '11.2' '11.5' '12.4' '11.5' '15.2' '16.3' '14.1' '15.0' '15.5' '13.6'
 '11.4' '13.0' '12.0' '9.9' '8.8' '10.1' '10.5' '8.5' '9.1' '10.9' '19.1'
 '22.2' '24.0' '26.7' '25.7' '27.4' '29.2' '30.5' '22.1' '17.2' '18.9'
 '19.2' '17.4' '14.0' '15.5' '13.5' '15.4' '14.2' '15.6' '17.4' '18.0'
 '18.9' '19.3' '17.8' '16.3' '16.8' '17.3' '18.8' '20.3' '19.0' '19.3'
 '21.1' '20.6' '19.1' '18.3' '16.3' '12.0' '13.1' '14.2' '15.0' '13.6'
 '15.5' '12.3' '11.7' '11.2' '11.7' '13.2' '12.6' '10.2' '10.7' '13.3'
 '11.4' '12.4' '13.7' '18.4' '14.9' '17.0' '18.2' '21.2' '24.3' '23.0'
 '24.3' '22.4' '21.2' '20.5' '23.8' '23.8' '24.2' '24.4' '26.0' '23.5'
 '24.2' '22.8' '19.6' '20.4' '21.0' '21.2' '20.7' '22.4' '24.4' '26.4'
 '27.6' '24.4' '23.6' '24.6' '25.9' '22.0' '22.3' '22.5' '23.3' '24.8'
 '27.3' '28.9' '29.0' '26.1' '27.0' '24.1' '23.5' '21.8' '20.1' '18.1'
 '20.8' '21.3' '22.3' '29.0' '30.9' '29.0' '25.6' '25.8' '26.2' '26.9'
 '26.2' '30.0' '29.8' '30.8' '26.8' '27.5' '26.2' '27.7' '27.0' '26.6'
 '25.3' '28.1' '30.6' '28.0' '29.0' '29.6' '26.6' '24.8' '26.5' '27.5'
 '29.7' '26.4' '24.8' '25.5' '26.9' '28.5' '27.6' '27.8' '29.0' '28.8'
 '29.7' '27.7' '25.5' '27.2' '27.6' '26.7' '22.7' '19.0' '19.6' '18.5'
 '17.8' '14.6' '15.9' '19.5' '20.5' '22.5' '24.7' '23.9' '21.5' '21.7'
 '20.5' '19.0' '19.5' '19.9' '18.5' '20.2' '18.0' '20.3' '17.8' '16.7'
 '15.0' '15.1' '16.1' '14.5' '17.4' '15.7' '14.3' '15.2' '14.6' '14.8'
 '16.0' '16.0' '16.3' '15.9' '14.8' '12.8' '10.3' '12.5' '12.1' '18.4'
 '17.5' '18.2' '18.8' '17.1' '18.1' '15.0' '15.6' '13.7' '15.8' '14.6'
 '14.8' '13.2' '14.3' '14.9' '12.6' '7.0' '9.2' '12.3' '18.7' '20.6'
 '21.7' '22.1' '22.2' '23.5' '21.4' '19.2' '20.8' '21.2' '22.6' '19.2'
 '22.2' '22.5' '17.7' '17.2' '19.9' '19.9' '19.6' '18.1' '18.4' '21.1'
 '22.6' '18.3' '20.3' '22.2' '18.3' '21.5']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['9.9' '9.2' '8.9' '7.2' '7.2' '8.1' '5.8' '7.5' '7.3' '6.0' '3.7' '2.5'
 '3.5' '5.7' '9.0' '12.1' '12.0' '14.0' '19.0' '18.2' '19.7' '21.7' '23.5'
 '25.6' '24.0' '24.4' '23.0' '18.2' '16.2' '16.4' '17.2' '19.0' '17.0'
 '13.1' '11.4' '11.8' '10.9' '7.9' '6.6' '6.8' '5.7' '6.5' '7.9' '10.3'
 '12.3' '10.3' '8.4' '6.8' '5.2' '4.5' '6.4' '8.5' '8.2' '11.1' '7.4'
 '6.6' '5.4' '4.5' '5.1' '7.8' '8.2' '9.3' '10.8' '8.6' '8.7' '7.4' '8.0'
 '10.5' '9.3' '9.8' '7.0' '8.2' '14.0' '15.1' '18.5' '18.3' '13.0' '13.7'
 '13.2' '10.0' '7.7' '9.6' '12.7' '9.8' '11.9' '13.8' '12.6' '13.5' '11.1'
 '13.3' '10.6' '12.0' '10.0' '10.3' '10.1' '7.6' '9.2' '8.5' '9.6' '9.5'
 '12.0' '16.2' '20.0' '21.2' '17.1' '19.2' '21.6' '20.5' '21.7' '18.7'
 '19.7' '20.2' '14.4' '13.0' '15.0' '14.7' '15.6' '12.5' '11.1' '9.2'
 '11.1' '14.2' '16.8' '18.0' '11.5' '14.2' '13.4' '13.3' '15.3' '12.5'
 '15.1' '16.0' '17.2' '18.3' '19.6' '20.4' '12.2' '13.6' '12.0' '7.1'
 '6.5' '8.3' '11.0' '13.4' '14.4' '13.2' '12.4' '7.9' '9.5' '11.2' '10.0'
 '11.2' '11.5' '12.4' '11.5' '15.2' '16.3' '14.1' '15.0' '15.5' '13.6'
 '11.4' '13.0' '12.0' '9.9' '8.8' '10.1' '10.5' '8.5' '9.1' '10.9' '19.1'
 '22.2' '24.0' '26.7' '25.7' '27.4' '29.2' '30.5' '22.1' '17.2' '18.9'
 '19.2' '17.4' '14.0' '15.5' '13.5' '15.4' '14.2' '15.6' '17.4' '18.0'
 '18.9' '19.3' '17.8' '16.3' '16.8' '17.3' '18.8' '20.3' '19.0' '19.3'
 '21.1' '20.6' '19.1' '18.3' '16.3' '12.0' '13.1' '14.2' '15.0' '13.6'
 '15.5' '12.3' '11.7' '11.2' '11.7' '13.2' '12.6' '10.2' '10.7' '13.3'
 '11.4' '12.4' '13.7' '18.4' '14.9' '17.0' '18.2' '21.2' '24.3' '23.0'
 '24.3' '22.4' '21.2' '20.5' '23.8' '23.8' '24.2' '24.4' '26.0' '23.5'
 '24.2' '22.8' '19.6' '20.4' '21.0' '21.2' '20.7' '22.4' '24.4' '26.4'
 '27.6' '24.4' '23.6' '24.6' '25.9' '22.0' '22.3' '22.5' '23.3' '24.8'
 '27.3' '28.9' '29.0' '26.1' '27.0' '24.1' '23.5' '21.8' '20.1' '18.1'
 '20.8' '21.3' '22.3' '29.0' '30.9' '29.0' '25.6' '25.8' '26.2' '26.9'
 '26.2' '30.0' '29.8' '30.8' '26.8' '27.5' '26.2' '27.7' '27.0' '26.6'
 '25.3' '28.1' '30.6' '28.0' '29.0' '29.6' '26.6' '24.8' '26.5' '27.5'
 '29.7' '26.4' '24.8' '25.5' '26.9' '28.5' '27.6' '27.8' '29.0' '28.8'
 '29.7' '27.7' '25.5' '27.2' '27.6' '26.7' '22.7' '19.0' '19.6' '18.5'
 '17.8' '14.6' '15.9' '19.5' '20.5' '22.5' '24.7' '23.9' '21.5' '21.7'
 '20.5' '19.0' '19.5' '19.9' '18.5' '20.2' '18.0' '20.3' '17.8' '16.7'
 '15.0' '15.1' '16.1' '14.5' '17.4' '15.7' '14.3' '15.2' '14.6' '14.8'
 '16.0' '16.0' '16.3' '15.9' '14.8' '12.8' '10.3' '12.5' '12.1' '18.4'
 '17.5' '18.2' '18.8' '17.1' '18.1' '15.0' '15.6' '13.7' '15.8' '14.6'
 '14.8' '13.2' '14.3' '14.9' '12.6' '7.0' '9.2' '12.3' '18.7' '20.6'
 '21.7' '22.1' '22.2' '23.5' '21.4' '19.2' '20.8' '21.2' '22.6' '19.2'
 '22.2' '22.5' '17.7' '17.2' '19.9' '19.9' '19.6' '18.1' '18.4' '21.1'
 '22.6' '18.3' '20.3' '22.2' '18.3' '21.5']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['63.2' '58.6' '56.0' '59.8' '56.8' '56.5' '50.3' '51.7' '51.8' '55.0'
 '51.1' '51.5' '55.8' '51.9' '49.6' '45.0' '48.1' '57.9' '58.4' '59.8'
 '63.9' '65.6' '70.0' '62.3' '64.2' '66.4' '65.3' '61.1' '62.9' '61.8'
 '64.3' '62.7' '60.7' '59.3' '60.3' '56.6' '62.5' '58.5' '51.7' '54.3'
 '53.7' '50.2' '51.9' '56.4' '56.1' '60.8' '59.7' '61.9' '55.8' '51.5'
 '52.5' '57.6' '59.1' '52.3' '55.7' '58.3' '54.6' '54.6' '50.7' '53.5'
 '49.0' '52.8' '54.9' '46.5' '51.8' '47.4' '45.1' '48.0' '54.7' '49.8'
 '62.7' '70.7' '70.3' '73.7' '75.4' '82.7' '75.5' '80.0' '88.6' '93.2'
 '95.3' '97.5' '100.4' '99.6' '96.3' '92.9' '81.1' '82.6' '79.8' '77.0'
 '68.5' '69.0' '62.1' '57.2' '53.8' '58.4' '54.1' '52.1' '60.3' '55.6'
 '53.7' '65.2' '64.8' '71.5' '68.3' '69.1' '77.9' '77.1' '76.9' '75.3'
 '70.9' '71.7' '79.0' '83.9' '78.5' '83.4' '90.2' '89.3' '92.4' '115.4'
 '119.1' '120.6' '131.5' '130.1' '135.6' '135.7' '138.9' '143.5' '143.6'
 '141.8' '136.8' '136.1' '133.9' '131.1' '133.8' '133.3' '135.3' '130.8'
 '125.1' '118.2' '118.8' '110.6' '104.1' '102.7' '108.1' '107.2' '91.8'
 '95.3' '89.2' '79.3' '74.2' '65.6' '63.9' '61.1' '58.4' '59.0' '56.5'
 '51.4' '50.2' '43.7' '44.9' '41.2' '39.4' '50.9' '49.6' '44.5' '44.7'
 '67.0' '71.6' '64.6' '73.8' '85.0' '95.1' '105.0' '110.7' '116.3' '123.5'
 '125.7' '125.3' '117.4' '125.9' '129.9' '132.8' '137.7' '138.1' '138.3'
 '137.2' '140.4' '140.4' '146.3' '148.3' '151.4' '148.1' '147.3' '148.7'
 '145.3' '145.4' '140.9' '148.7' '140.0' '135.2' '137.6' '128.3' '130.2'
 '127.6' '122.6' '117.0' '116.4' '115.1' '109.9' '107.4' '101.4' '97.6'
 '83.5' '85.2' '81.7' '73.7' '78.1' '67.6' '66.9' '68.2' '68.3' '67.0'
 '68.6' '64.2' '62.8' '63.5' '58.8' '55.6' '54.2' '57.6' '54.3' '56.4'
 '57.7' '53.5' '52.6' '54.0' '57.1' '59.2' '59.6' '65.1' '68.3' '62.7'
 '57.8' '56.3' '54.1' '51.9' '53.8' '58.6' '58.3' '55.2' '58.9' '51.6'
 '52.6' '49.8' '52.3' '55.0' '60.6' '59.0' '63.8' '60.1' '56.6' '59.9'
 '63.7' '64.3' '72.3' '68.5' '70.9' '72.3' '67.9' '67.4' '78.7' '82.3'
 '88.9' '94.5' '97.0' '95.9' '97.7' '96.4' '100.2' '108.6' '108.7' '117.3'
 '117.5' '118.3' '118.5' '120.6' '118.7' '117.0' '117.8' '123.5' '125.1'
 '123.7' '118.9' '116.0' '116.4' '124.8' '118.0' '119.7' '111.3' '106.8'
 '96.8' '91.1' '90.0' '82.2' '88.8' '89.2' '92.3' '83.4' '84.0' '87.8'
 '86.4' '85.0' '92.0' '88.8' '96.1' '98.0' '90.2' '93.3' '90.3' '92.1'
 '110.1' '109.2' '104.7' '106.1' '105.8' '114.3' '110.2' '110.3' '110.4'
 '112.3' '108.2' '111.3' '114.3' '106.0' '103.1' '101.4' '106.0' '101.2'
 '102.2' '98.6' '97.2' '97.6' '102.8' '104.9' '108.7' '104.9' '108.1'
 '107.0' '114.3' '115.5' '116.7' '119.6' '117.8' '111.8' '115.4' '117.4'
 '118.5' '115.9' '127.6' '126.5' '125.6' '126.8' '124.2' '123.6' '124.0'
 '116.0' '113.6' '107.1' '103.8' '97.2' '93.2' '82.1' '80.0' '80.0' '83.7'
 '79.7' '79.6' '86.1' '91.1' '88.0' '85.6' '88.2' '88.7' '89.5' '81.4'
 '76.9' '79.1' '74.1' '74.0' '77.6' '76.1' '74.9' '74.0' '66.7' '64.7'
 '63.5' '59.3' '66.6' '66.7' '65.8' '73.6' '67.4' '62.2']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['63.2' '58.6' '56.0' '59.8' '56.8' '56.5' '50.3' '51.7' '51.8' '55.0'
 '51.1' '51.5' '55.8' '51.9' '49.6' '45.0' '48.1' '57.9' '58.4' '59.8'
 '63.9' '65.6' '70.0' '62.3' '64.2' '66.4' '65.3' '61.1' '62.9' '61.8'
 '64.3' '62.7' '60.7' '59.3' '60.3' '56.6' '62.5' '58.5' '51.7' '54.3'
 '53.7' '50.2' '51.9' '56.4' '56.1' '60.8' '59.7' '61.9' '55.8' '51.5'
 '52.5' '57.6' '59.1' '52.3' '55.7' '58.3' '54.6' '54.6' '50.7' '53.5'
 '49.0' '52.8' '54.9' '46.5' '51.8' '47.4' '45.1' '48.0' '54.7' '49.8'
 '62.7' '70.7' '70.3' '73.7' '75.4' '82.7' '75.5' '80.0' '88.6' '93.2'
 '95.3' '97.5' '100.4' '99.6' '96.3' '92.9' '81.1' '82.6' '79.8' '77.0'
 '68.5' '69.0' '62.1' '57.2' '53.8' '58.4' '54.1' '52.1' '60.3' '55.6'
 '53.7' '65.2' '64.8' '71.5' '68.3' '69.1' '77.9' '77.1' '76.9' '75.3'
 '70.9' '71.7' '79.0' '83.9' '78.5' '83.4' '90.2' '89.3' '92.4' '115.4'
 '119.1' '120.6' '131.5' '130.1' '135.6' '135.7' '138.9' '143.5' '143.6'
 '141.8' '136.8' '136.1' '133.9' '131.1' '133.8' '133.3' '135.3' '130.8'
 '125.1' '118.2' '118.8' '110.6' '104.1' '102.7' '108.1' '107.2' '91.8'
 '95.3' '89.2' '79.3' '74.2' '65.6' '63.9' '61.1' '58.4' '59.0' '56.5'
 '51.4' '50.2' '43.7' '44.9' '41.2' '39.4' '50.9' '49.6' '44.5' '44.7'
 '67.0' '71.6' '64.6' '73.8' '85.0' '95.1' '105.0' '110.7' '116.3' '123.5'
 '125.7' '125.3' '117.4' '125.9' '129.9' '132.8' '137.7' '138.1' '138.3'
 '137.2' '140.4' '140.4' '146.3' '148.3' '151.4' '148.1' '147.3' '148.7'
 '145.3' '145.4' '140.9' '148.7' '140.0' '135.2' '137.6' '128.3' '130.2'
 '127.6' '122.6' '117.0' '116.4' '115.1' '109.9' '107.4' '101.4' '97.6'
 '83.5' '85.2' '81.7' '73.7' '78.1' '67.6' '66.9' '68.2' '68.3' '67.0'
 '68.6' '64.2' '62.8' '63.5' '58.8' '55.6' '54.2' '57.6' '54.3' '56.4'
 '57.7' '53.5' '52.6' '54.0' '57.1' '59.2' '59.6' '65.1' '68.3' '62.7'
 '57.8' '56.3' '54.1' '51.9' '53.8' '58.6' '58.3' '55.2' '58.9' '51.6'
 '52.6' '49.8' '52.3' '55.0' '60.6' '59.0' '63.8' '60.1' '56.6' '59.9'
 '63.7' '64.3' '72.3' '68.5' '70.9' '72.3' '67.9' '67.4' '78.7' '82.3'
 '88.9' '94.5' '97.0' '95.9' '97.7' '96.4' '100.2' '108.6' '108.7' '117.3'
 '117.5' '118.3' '118.5' '120.6' '118.7' '117.0' '117.8' '123.5' '125.1'
 '123.7' '118.9' '116.0' '116.4' '124.8' '118.0' '119.7' '111.3' '106.8'
 '96.8' '91.1' '90.0' '82.2' '88.8' '89.2' '92.3' '83.4' '84.0' '87.8'
 '86.4' '85.0' '92.0' '88.8' '96.1' '98.0' '90.2' '93.3' '90.3' '92.1'
 '110.1' '109.2' '104.7' '106.1' '105.8' '114.3' '110.2' '110.3' '110.4'
 '112.3' '108.2' '111.3' '114.3' '106.0' '103.1' '101.4' '106.0' '101.2'
 '102.2' '98.6' '97.2' '97.6' '102.8' '104.9' '108.7' '104.9' '108.1'
 '107.0' '114.3' '115.5' '116.7' '119.6' '117.8' '111.8' '115.4' '117.4'
 '118.5' '115.9' '127.6' '126.5' '125.6' '126.8' '124.2' '123.6' '124.0'
 '116.0' '113.6' '107.1' '103.8' '97.2' '93.2' '82.1' '80.0' '80.0' '83.7'
 '79.7' '79.6' '86.1' '91.1' '88.0' '85.6' '88.2' '88.7' '89.5' '81.4'
 '76.9' '79.1' '74.1' '74.0' '77.6' '76.1' '74.9' '74.0' '66.7' '64.7'
 '63.5' '59.3' '66.6' '66.7' '65.8' '73.6' '67.4' '62.2']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['7.1' '7.6' '6.6' '6.2' '7.4' '8.7' '3.5' '5.6' '4.8' '2.2' '2.0' '-0.2'
 '1.5' '0.5' '3.0' '4.1' '4.9' '12.2' '8.4' '9.0' '9.9' '11.5' '14.8'
 '17.0' '19.1' '20.4' '16.6' '14.3' '12.8' '14.4' '15.7' '15.0' '5.5'
 '7.2' '9.1' '8.1' '7.5' '8.8' '7.6' '4.7' '3.5' '4.8' '5.9' '7.3' '6.7'
 '7.0' '6.1' '4.0' '3.6' '4.7' '3.3' '4.8' '7.7' '3.4' '5.0' '4.5' '1.2'
 '0.5' '2.7' '4.5' '4.7' '7.5' '6.3' '5.9' '5.0' '3.2' '4.1' '3.7' '5.8'
 '8.1' '12.0' '13.5' '14.9' '16.0' '13.7' '13.0' '14.7' '12.0' '8.8'
 '10.0' '11.4' '10.4' '11.7' '11.9' '10.0' '10.8' '11.6' '12.8' '5.5'
 '5.0' '6.2' '3.5' '3.9' '4.5' '5.1' '4.7' '5.4' '5.2' '6.5' '8.0' '9.7'
 '10.8' '7.2' '7.9' '8.8' '9.6' '11.1' '7.0' '8.2' '9.4' '2.3' '3.8' '5.0'
 '3.7' '4.7' '0.2' '1.4' '0.0' '2.6' '5.9' '7.6' '6.0' '5.2' '6.5' '6.6'
 '7.8' '8.9' '6.6' '6.4' '8.8' '11.0' '12.3' '14.0' '16.3' '12.6' '13.4'
 '8.8' '6.0' '4.1' '2.8' '3.7' '4.5' '7.2' '5.5' '6.7' '-1.8' '0.8' '3.5'
 '0.0' '1.2' '1.1' '2.9' '2.9' '7.2' '8.7' '5.5' '6.9' '7.3' '5.5' '4.7'
 '5.3' '3.5' '0.0' '0.0' '0.6' '0.0' '-1.0' '1.4' '4.4' '11.7' '14.0'
 '16.4' '8.7' '10.5' '7.8' '9.6' '9.5' '10.8' '9.7' '3.0' '-0.3' '1.0'
 '1.0' '1.2' '-1.0' '1.2' '3.0' '-2.3' '-1.9' '0.0' '3.0' '4.5' '6.0'
 '7.4' '5.2' '6.6' '2.8' '4.3' '4.4' '5.6' '3.4' '5.4' '6.3' '7.6' '6.0'
 '4.5' '-1.0' '-4.2' '-1.8' '-0.2' '-2.2' '-2.5' '-3.2' '-5.0' '-3.1'
 '-3.4' '-1.0' '-3.0' '-6.0' '-4.4' '-3.0' '-3.5' '-3.4' '-3.0' '-1.5'
 '-5.2' '-3.3' '-1.9' '-0.9' '-0.3' '-2.0' '-1.1' '-2.0' '-0.5' '-1.8'
 '-0.8' '1.0' '3.4' '4.8' '3.6' '5.0' '4.1' '1.9' '3.8' '3.8' '4.1' '5.5'
 '6.4' '7.2' '8.2' '8.3' '7.9' '6.3' '7.5' '6.5' '5.5' '5.6' '5.0' '5.2'
 '6.6' '7.8' '7.9' '8.7' '6.0' '1.4' '3.0' '3.2' '4.0' '3.7' '3.4' '4.0'
 '5.0' '7.0' '9.1' '13.0' '14.2' '14.3' '10.2' '9.7' '10.9' '10.7' '12.3'
 '12.8' '12.6' '12.8' '12.8' '10.4' '10.1' '10.0' '9.5' '9.8' '10.1'
 '10.3' '11.1' '11.7' '9.8' '9.4' '10.9' '9.8' '10.4' '11.6' '11.5' '12.1'
 '13.1' '12.3' '13.6' '13.8' '13.6' '13.6' '10.4' '12.7' '13.3' '12.3'
 '12.9' '11.0' '10.2' '11.1' '12.4' '13.0' '11.6' '11.3' '12.2' '13.8'
 '15.3' '12.2' '12.0' '17.1' '17.7' '17.6' '13.9' '10.1' '10.1' '10.9'
 '8.5' '9.5' '10.8' '12.2' '11.7' '12.3' '12.1' '12.8' '11.5' '8.5' '9.0'
 '9.0' '9.8' '8.3' '10.1' '9.6' '9.2' '10.8' '11.4' '12.1' '13.6' '13.2'
 '12.4' '12.5' '12.0' '13.0' '11.2' '12.4' '11.7' '17.1' '16.3' '16.4'
 '15.3' '15.0' '15.5' '15.6' '16.8' '15.1' '14.9' '14.2' '14.7' '15.3'
 '16.2' '16.2' '16.0' '14.5' '15.2' '15.9' '19.0' '19.4' '19.8' '20.2'
 '20.0' '21.0' '15.5' '13.0' '13.5' '9.2' '9.8' '10.1' '11.2' '10.3'
 '11.5' '12.3' '12.8' '12.3' '12.1' '12.4' '13.6' '16.0' '17.3' '18.8'
 '18.2' '19.5' '19.4' '19.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['7.1' '7.6' '6.6' '6.2' '7.4' '8.7' '3.5' '5.6' '4.8' '2.2' '2.0' '-0.2'
 '1.5' '0.5' '3.0' '4.1' '4.9' '12.2' '8.4' '9.0' '9.9' '11.5' '14.8'
 '17.0' '19.1' '20.4' '16.6' '14.3' '12.8' '14.4' '15.7' '15.0' '5.5'
 '7.2' '9.1' '8.1' '7.5' '8.8' '7.6' '4.7' '3.5' '4.8' '5.9' '7.3' '6.7'
 '7.0' '6.1' '4.0' '3.6' '4.7' '3.3' '4.8' '7.7' '3.4' '5.0' '4.5' '1.2'
 '0.5' '2.7' '4.5' '4.7' '7.5' '6.3' '5.9' '5.0' '3.2' '4.1' '3.7' '5.8'
 '8.1' '12.0' '13.5' '14.9' '16.0' '13.7' '13.0' '14.7' '12.0' '8.8'
 '10.0' '11.4' '10.4' '11.7' '11.9' '10.0' '10.8' '11.6' '12.8' '5.5'
 '5.0' '6.2' '3.5' '3.9' '4.5' '5.1' '4.7' '5.4' '5.2' '6.5' '8.0' '9.7'
 '10.8' '7.2' '7.9' '8.8' '9.6' '11.1' '7.0' '8.2' '9.4' '2.3' '3.8' '5.0'
 '3.7' '4.7' '0.2' '1.4' '0.0' '2.6' '5.9' '7.6' '6.0' '5.2' '6.5' '6.6'
 '7.8' '8.9' '6.6' '6.4' '8.8' '11.0' '12.3' '14.0' '16.3' '12.6' '13.4'
 '8.8' '6.0' '4.1' '2.8' '3.7' '4.5' '7.2' '5.5' '6.7' '-1.8' '0.8' '3.5'
 '0.0' '1.2' '1.1' '2.9' '2.9' '7.2' '8.7' '5.5' '6.9' '7.3' '5.5' '4.7'
 '5.3' '3.5' '0.0' '0.0' '0.6' '0.0' '-1.0' '1.4' '4.4' '11.7' '14.0'
 '16.4' '8.7' '10.5' '7.8' '9.6' '9.5' '10.8' '9.7' '3.0' '-0.3' '1.0'
 '1.0' '1.2' '-1.0' '1.2' '3.0' '-2.3' '-1.9' '0.0' '3.0' '4.5' '6.0'
 '7.4' '5.2' '6.6' '2.8' '4.3' '4.4' '5.6' '3.4' '5.4' '6.3' '7.6' '6.0'
 '4.5' '-1.0' '-4.2' '-1.8' '-0.2' '-2.2' '-2.5' '-3.2' '-5.0' '-3.1'
 '-3.4' '-1.0' '-3.0' '-6.0' '-4.4' '-3.0' '-3.5' '-3.4' '-3.0' '-1.5'
 '-5.2' '-3.3' '-1.9' '-0.9' '-0.3' '-2.0' '-1.1' '-2.0' '-0.5' '-1.8'
 '-0.8' '1.0' '3.4' '4.8' '3.6' '5.0' '4.1' '1.9' '3.8' '3.8' '4.1' '5.5'
 '6.4' '7.2' '8.2' '8.3' '7.9' '6.3' '7.5' '6.5' '5.5' '5.6' '5.0' '5.2'
 '6.6' '7.8' '7.9' '8.7' '6.0' '1.4' '3.0' '3.2' '4.0' '3.7' '3.4' '4.0'
 '5.0' '7.0' '9.1' '13.0' '14.2' '14.3' '10.2' '9.7' '10.9' '10.7' '12.3'
 '12.8' '12.6' '12.8' '12.8' '10.4' '10.1' '10.0' '9.5' '9.8' '10.1'
 '10.3' '11.1' '11.7' '9.8' '9.4' '10.9' '9.8' '10.4' '11.6' '11.5' '12.1'
 '13.1' '12.3' '13.6' '13.8' '13.6' '13.6' '10.4' '12.7' '13.3' '12.3'
 '12.9' '11.0' '10.2' '11.1' '12.4' '13.0' '11.6' '11.3' '12.2' '13.8'
 '15.3' '12.2' '12.0' '17.1' '17.7' '17.6' '13.9' '10.1' '10.1' '10.9'
 '8.5' '9.5' '10.8' '12.2' '11.7' '12.3' '12.1' '12.8' '11.5' '8.5' '9.0'
 '9.0' '9.8' '8.3' '10.1' '9.6' '9.2' '10.8' '11.4' '12.1' '13.6' '13.2'
 '12.4' '12.5' '12.0' '13.0' '11.2' '12.4' '11.7' '17.1' '16.3' '16.4'
 '15.3' '15.0' '15.5' '15.6' '16.8' '15.1' '14.9' '14.2' '14.7' '15.3'
 '16.2' '16.2' '16.0' '14.5' '15.2' '15.9' '19.0' '19.4' '19.8' '20.2'
 '20.0' '21.0' '15.5' '13.0' '13.5' '9.2' '9.8' '10.1' '11.2' '10.3'
 '11.5' '12.3' '12.8' '12.3' '12.1' '12.4' '13.6' '16.0' '17.3' '18.8'
 '18.2' '19.5' '19.4' '19.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5616.36' '5616.36' '5609.09' '5510.0' '5395.0' '5405.91' '5405.91'
 '5405.91' '5377.73' '5376.36' '5376.36' '5376.36' '5535.45' '5535.45'
 '5535.45' '5535.45' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27'
 '5537.27' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27'
 '5537.27' '5779.55' '5748.18' '5763.64' '5763.64' '5763.64' '5763.64'
 '5763.64' '5617.73' '5617.73' '5617.73' '5618.18' '5618.18' '5618.18'
 '5618.18' '5480.0' '5481.82' '5481.82' '5522.73' '5522.73' '5522.73'
 '5522.73' '5496.36' '5532.73' '5619.09' '5619.09' '5645.45' '5645.45'
 '5645.45' '5597.27' '5597.27' '5597.27' '5726.0' '5771.45' '5771.45'
 '5771.45' '5790.0' '5790.0' '5790.0' '5872.55' '5868.0' '5868.0' '5868.0'
 '5897.09' '5750.0' '5732.27' '5698.18' '5827.27' '5827.27' '5827.27'
 '5914.55' '5987.27' '6009.09' '6063.64' '6167.27' '6167.27' '6167.27'
 '6214.91' '6294.91' '6294.91' '6358.18' '6404.55' '6404.55' '6404.55'
 '6465.45' '6486.36' '6486.36' '6454.55' '6454.55' '6454.55' '6454.55'
 '6350.91' '6360.0' '6230.91' '6246.36' '6370.91' '6370.91' '6370.91'
 '6520.91' '6493.64' '6488.64' '6419.09' '6390.91' '6390.91' '6390.91'
 '6321.82' '6280.91' '6259.09' '6180.0' '6180.0' '6180.0' '6180.0'
 '6180.0' '6180.0' '6180.0' '6079.09' '5984.55' '5993.64' '5993.64'
 '6013.64' '6013.64' '6013.64' '6013.64' '5680.0' '5680.0' '5680.0'
 '5680.0' '5529.09' '5529.09' '5539.09' '5590.0' '5590.0' '5590.0'
 '5560.36' '5589.45' '5589.45' '5599.09' '5590.91' '5590.91' '5590.91'
 '5614.55' '5614.55' '5614.55' '5619.09' '5670.0' '5670.0' '5670.0'
 '5670.0' '5684.55' '5676.36' '5661.82' '5661.82' '5661.82' '5661.82'
 '5596.36' '5626.36' '5706.36' '5737.27' '5737.27' '5737.27' '5737.27'
 '5685.45' '5675.45' '5695.45' '5695.45' '5695.45' '5695.45' '5690.0'
 '5594.55' '5631.82' '5631.82' '5600.0' '5600.0' '5600.0' '5600.0'
 '5650.91' '5678.55' '5678.55' '5678.55' '5745.82' '5745.82' '5745.82'
 '5745.82' '5814.0' '5910.0' '5910.0' '5910.0' '5980.91' '5980.91'
 '5909.09' '5882.73' '5899.09' '5899.09' '5910.91' '5910.91' '5910.91'
 '5898.64' '5898.64' '5916.82' '5950.0' '5986.36' '6000.91' '6000.91'
 '6001.82' '6036.36' '6036.36' '5880.91' '5872.73' '5872.73' '5872.73'
 '5877.27' '5848.18' '5870.0' '5879.09' '5914.55' '5914.55' '5914.55'
 '5914.55' '5910.0' '5875.0' '5874.55' '5872.73' '5896.36' '5896.36'
 '6019.09' '6067.27' '6074.09' '6062.27' '6081.36' '6081.36' '6081.36'
 '6081.36' '6080.91' '6164.55' '6180.91' '6200.0' '6200.0' '6200.0'
 '6188.18' '6173.64' '6200.91' '6188.18' '6188.18' '6198.0' '6198.0'
 '6200.0' '6342.0' '6329.0' '6386.0' '6439.0' '6439.0' '6439.0' '6410.0'
 '6389.0' '6346.0' '6300.0' '6300.0' '6289.0' '6289.0' '6276.82' '6230.45'
 '6204.55' '6213.64' '6213.64' '6213.64' '6213.64' '6213.64' '6213.64'
 '6213.64' '6213.64' '6213.64' '6213.64' '6213.64' '6013.64' '5964.55'
 '5961.82' '5898.0' '5854.5' '5854.5' '5854.5' '5871.0' '5870.0' '5867.5'
 '5856.0' '5851.0' '5851.0' '5851.0' '5851.0' '5851.0' '5851.5' '5873.0'
 '5875.5' '5875.5' '5875.5' '5915.0' '5915.0' '5937.0' '5937.0' '5938.0'
 '5938.0' '5938.0' '5916.0' '5903.0' '5888.0' '5888.0' '5893.5' '5893.5'
 '5893.5' '5869.44' '5909.44' '5937.22' '6025.0' '6026.5' '6026.5'
 '6026.5' '6049.0' '5879.7' '5879.7' '5825.7' '5825.7' '5825.7' '5825.7'
 '5775.0' '5692.0' '5675.0' '5675.0' '5675.0' '5675.0' '5675.0' '5681.5'
 '5681.5' '5681.5' '5694.0' '5745.0' '5745.0' '5745.0' '5728.5' '5728.5'
 '5657.5' '5657.5' '5666.5' '5666.5' '5666.5' '5712.5' '5727.5' '5727.5'
 '5727.5' '5874.0' '5874.0' '5874.0' '5874.0' '5910.0' '5900.0' '5953.0'
 '5930.0' '5930.0' '5930.0' '5930.0' '5942.5' '5896.0' '5896.0' '5893.5'
 '5893.5' '5893.5' '5893.5' '5766.0' '5723.5' '5718.0' '5718.0' '5731.0'
 '5731.0' '5753.0' '5794.0' '5796.0' '5824.0' '5854.0' '5854.0' '5854.0'
 '5884.9' '5884.9' '5902.0' '6007.0' '6007.0' '6007.0' '6007.0' '6007.0'
 '6001.0' '5994.0' '5994.0' '6020.0' '5971.11' '5950.0' '5950.0' '5945.0'
 '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0'
 '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5929.0' '5929.0' '5943.0'
 '5941.0' '5941.0' '5941.0' '5879.0' '5911.0' '5911.0' '5950.0' '5950.0'
 '5950.0' '5950.0' '5930.5' '5873.5' '5878.5' '5869.0' '5839.0' '5839.0'
 '5839.0' '5839.0' '5806.0' '5809.0' '5835.0' '5883.0' '5883.0' '5883.0'
 '5931.0' '5933.0' '5923.0' '5923.0' '5813.0' '5813.0' '5813.0' '5813.0'
 '5833.0' '5833.0' '5833.0' '5859.0' '5859.0' '5859.0' '5994.0' '5994.0'
 '6015.5' '6015.5' '6015.5' '6015.5' '6015.5' '5999.0' '5996.0' '5996.0'
 '5990.0' '5990.0' '5924.0' '5924.0' '5959.0' '5964.0' '5964.0' '5964.0'
 '5948.0' '5948.0' '5948.0' '5947.0' '5945.5' '5945.5' '5935.5' '5940.5'
 '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5'
 '5940.5' '5940.5' '5878.0' '5878.0' '5853.5' '5844.5' '5844.5' '5842.5'
 '5842.5' '5804.0' '5818.0' '5838.5' '5858.5' '5858.5' '5858.5' '5915.0'
 '5915.0' '5923.0' '5918.0' '5946.0' '5946.0' '5946.0' '5976.0' '5976.0'
 '5968.0' '6010.0' '6010.0' '6010.0' '6010.0' '5985.5' '5911.0' '5941.0'
 '5941.0' '5958.0' '5957.0' '5957.0' '5957.0' '5987.0' '5987.0' '5987.0'
 '5982.0' '5977.0' '5977.0' '5956.0' '5923.5' '5953.0' '5985.0' '5976.0'
 '5976.0' '5976.0' '5973.7' '5973.7' '6008.0' '6007.0' '6025.0' '6025.0'
 '6025.0' '6025.0' '6051.0' '6114.0' '6104.0' '6104.0' '6104.0' '6104.0'
 '6048.0' '5991.0' '5969.0' '5978.0' '5982.0' '5982.0' '5982.0' '5946.0'
 '5946.0' '5931.0' '5939.0' '5931.0' '5931.0' '5931.0' '5901.67' '5888.33'
 '5885.56' '5831.11' '5844.44' '5844.44' '5844.44' '5850.0' '5850.0'
 '5781.11' '5830.56' '5824.44' '5824.44' '5824.44' '5715.56' '5657.78'
 '5630.0' '5616.67' '5616.67' '5616.67' '5616.67' '5599.11' '5610.22'
 '5598.89' '5540.0' '5566.11' '5566.11' '5566.11' '5446.67' '5357.78'
 '5357.78' '5346.11' '5335.56' '5335.56' '5335.56' '5388.0' '5414.0'
 '5414.0' '5383.0' '5383.0' '5383.0' '5383.0' '5303.5' '5149.0' '5149.0'
 '5046.0' '5012.0' '5012.0' '5012.0' '4906.0' '4757.5' '4747.5' '4778.5'
 '4792.5' '4792.5' '4792.5' '4792.5' '4792.5' '4792.5' '4761.5' '4802.0'
 '4811.0' '4811.0' '4777.0' '4777.0' '4858.0' '4878.0' '4888.0' '4888.0'
 '4888.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0'
 '4928.0' '5271.0' '5218.5' '5206.5' '5206.5' '5208.5' '5208.5' '5208.5'
 '5079.0' '5009.0' '4887.0' '4912.0' '4912.0' '4912.0' '4904.0' '4904.0'
 '4956.0' '4955.0' '4953.0' '4953.0' '4953.0' '4927.0' '4881.0' '4881.0'
 '4901.0' '4900.0' '4900.0' '4900.0' '4900.0' '4900.0' '4825.0' '4877.0'
 '4915.0' '4915.0' '4915.0' '4915.0' '4915.0' '4841.0' '4807.0' '4803.0'
 '4803.0' '4803.0' '4795.56' '4792.22' '4824.44' '4828.89' '4832.22'
 '4832.22' '4832.22' '4832.22' '4764.0' '4783.0' '4741.0' '4741.0'
 '4741.0' '4741.0' '4741.0' '4754.7' '4759.7' '4759.7' '4759.7' '4759.7'
 '4759.7' '4680.5' '4697.0' '4697.0' '4734.0' '4734.0' '4734.0' '4734.0'
 '4734.0' '4734.0' '4797.0' '4809.0' '4809.0' '4809.0' '4809.0' '4832.0'
 '4832.0' '4833.0' '4831.0' '4767.0' '4767.0' '4767.0' '4767.0' '4790.0'
 '4790.0' '4790.0' '4790.0' '4795.56' '4795.56' '4795.56' '4795.56'
 '4803.89' '4871.67' '4893.89' '4893.89' '4893.89' '4963.89' '4996.11'
 '5084.44' '5084.44' '5084.44' '5084.44' '5084.44' '5081.11' '5081.11'
 '5068.89' '5048.89' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22'
 '4992.22' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22'
 '4954.0' '4956.82' '5023.27' '5023.27' '5023.27' '5118.18' '5115.91'
 '5115.91' '5077.73' '5084.09' '5084.09' '5084.09' '5104.09' '5123.18'
 '5121.82' '5122.73' '5091.82' '5091.82' '5091.82' '5036.36' '5032.73'
 '5032.73' '4967.27' '4967.27' '4967.27' '4967.27' '4993.64' '4998.18'
 '4881.82' '4881.82' '4876.55' '4876.55' '4876.55' '4850.91' '4796.36'
 '4748.18' '4748.18' '4797.45' '4797.45' '4797.45' '4816.91' '4855.64'
 '4846.36' '4848.82' '4877.27' '4877.27' '4877.27' '4877.27' '4889.8'
 '4911.4' '4912.9' '4906.6' '4906.6' '4906.6' '4861.6' '4910.0' '4919.0'
 '4868.0' '4868.0' '4868.0' '4868.0' '4722.5' '4672.5' '4518.5' '4489.0'
 '4440.5' '4338.12' '4338.12' '4368.75' '4328.75' '4253.75' '4318.75'
 '4320.62' '4320.62' '4320.62' '4330.0' '4316.25' '4366.88' '4360.0'
 '4493.75' '4493.75' '4493.75' '4567.5' '4565.0' '4584.38' '4584.38'
 '4584.38' '4584.38' '4584.38' '4582.5' '4529.17' '4555.84' '4688.34'
 '4750.84' '4750.84' '4750.84' '4888.33' '4913.34' '4913.34' '4991.66'
 '5007.5' '5007.5' '5007.5' '4993.75' '5001.25' '4986.25' '4913.34'
 '4915.0' '4915.0' '4915.0' '4917.5' '4938.75' '4893.75' '4972.5'
 '4965.62' '4965.62' '4965.62' '4965.62' '4948.75' '4896.5' '4880.66'
 '4903.25' '4903.25' '4903.25' '4884.5' '4880.5' '4871.75' '4880.75'
 '4932.75' '4932.75' '4932.75' '5008.25' '5045.0' '5165.5' '5246.5'
 '5271.5' '5271.5' '5271.5' '5282.0' '5114.0' '5114.0' '5083.25' '5084.75'
 '5084.75' '5084.75' '5104.75' '5081.66' '5031.25' '5031.25' '5031.25'
 '5031.25' '5031.25' '4977.5' '4930.84' '4852.5' '4852.5' '4852.5'
 '4852.5' '4852.5' '4789.84' '4735.66' '4735.66' '4762.66' '4776.66'
 '4776.66' '4776.66' '4825.0' '4817.0' '4831.34' '4831.34' '4874.84'
 '4874.84' '4874.84' '4862.5' '4863.34' '4889.16' '4859.59' '4821.25'
 '4821.25' '4821.25' '4808.75' '4765.0' '4771.25' '4771.25' '4742.5'
 '4742.5' '4742.5' '4750.0' '4731.88' '4731.88' '4717.5' '4726.88'
 '4726.88' '4726.88' '4720.25' '4726.0' '4727.0' '4771.0' '4771.0'
 '4771.0' '4771.0' '4855.0' '4922.5' '4913.75' '4910.0' '4891.25'
 '4891.25' '4891.25' '4755.5' '4759.5' '4749.5' '4740.5' '4739.5' '4739.5'
 '4739.5' '4720.0' '4643.5' '4649.16' '4649.16' '4636.66' '4636.66'
 '4636.66' '4631.66' '4633.75' '4649.0' '4644.0' '4631.25' '4631.25'
 '4631.25' '4626.25' '4618.75' '4572.91' '4635.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5616.36' '5616.36' '5609.09' '5510.0' '5395.0' '5405.91' '5405.91'
 '5405.91' '5377.73' '5376.36' '5376.36' '5376.36' '5535.45' '5535.45'
 '5535.45' '5535.45' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27'
 '5537.27' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27'
 '5537.27' '5779.55' '5748.18' '5763.64' '5763.64' '5763.64' '5763.64'
 '5763.64' '5617.73' '5617.73' '5617.73' '5618.18' '5618.18' '5618.18'
 '5618.18' '5480.0' '5481.82' '5481.82' '5522.73' '5522.73' '5522.73'
 '5522.73' '5496.36' '5532.73' '5619.09' '5619.09' '5645.45' '5645.45'
 '5645.45' '5597.27' '5597.27' '5597.27' '5726.0' '5771.45' '5771.45'
 '5771.45' '5790.0' '5790.0' '5790.0' '5872.55' '5868.0' '5868.0' '5868.0'
 '5897.09' '5750.0' '5732.27' '5698.18' '5827.27' '5827.27' '5827.27'
 '5914.55' '5987.27' '6009.09' '6063.64' '6167.27' '6167.27' '6167.27'
 '6214.91' '6294.91' '6294.91' '6358.18' '6404.55' '6404.55' '6404.55'
 '6465.45' '6486.36' '6486.36' '6454.55' '6454.55' '6454.55' '6454.55'
 '6350.91' '6360.0' '6230.91' '6246.36' '6370.91' '6370.91' '6370.91'
 '6520.91' '6493.64' '6488.64' '6419.09' '6390.91' '6390.91' '6390.91'
 '6321.82' '6280.91' '6259.09' '6180.0' '6180.0' '6180.0' '6180.0'
 '6180.0' '6180.0' '6180.0' '6079.09' '5984.55' '5993.64' '5993.64'
 '6013.64' '6013.64' '6013.64' '6013.64' '5680.0' '5680.0' '5680.0'
 '5680.0' '5529.09' '5529.09' '5539.09' '5590.0' '5590.0' '5590.0'
 '5560.36' '5589.45' '5589.45' '5599.09' '5590.91' '5590.91' '5590.91'
 '5614.55' '5614.55' '5614.55' '5619.09' '5670.0' '5670.0' '5670.0'
 '5670.0' '5684.55' '5676.36' '5661.82' '5661.82' '5661.82' '5661.82'
 '5596.36' '5626.36' '5706.36' '5737.27' '5737.27' '5737.27' '5737.27'
 '5685.45' '5675.45' '5695.45' '5695.45' '5695.45' '5695.45' '5690.0'
 '5594.55' '5631.82' '5631.82' '5600.0' '5600.0' '5600.0' '5600.0'
 '5650.91' '5678.55' '5678.55' '5678.55' '5745.82' '5745.82' '5745.82'
 '5745.82' '5814.0' '5910.0' '5910.0' '5910.0' '5980.91' '5980.91'
 '5909.09' '5882.73' '5899.09' '5899.09' '5910.91' '5910.91' '5910.91'
 '5898.64' '5898.64' '5916.82' '5950.0' '5986.36' '6000.91' '6000.91'
 '6001.82' '6036.36' '6036.36' '5880.91' '5872.73' '5872.73' '5872.73'
 '5877.27' '5848.18' '5870.0' '5879.09' '5914.55' '5914.55' '5914.55'
 '5914.55' '5910.0' '5875.0' '5874.55' '5872.73' '5896.36' '5896.36'
 '6019.09' '6067.27' '6074.09' '6062.27' '6081.36' '6081.36' '6081.36'
 '6081.36' '6080.91' '6164.55' '6180.91' '6200.0' '6200.0' '6200.0'
 '6188.18' '6173.64' '6200.91' '6188.18' '6188.18' '6198.0' '6198.0'
 '6200.0' '6342.0' '6329.0' '6386.0' '6439.0' '6439.0' '6439.0' '6410.0'
 '6389.0' '6346.0' '6300.0' '6300.0' '6289.0' '6289.0' '6276.82' '6230.45'
 '6204.55' '6213.64' '6213.64' '6213.64' '6213.64' '6213.64' '6213.64'
 '6213.64' '6213.64' '6213.64' '6213.64' '6213.64' '6013.64' '5964.55'
 '5961.82' '5898.0' '5854.5' '5854.5' '5854.5' '5871.0' '5870.0' '5867.5'
 '5856.0' '5851.0' '5851.0' '5851.0' '5851.0' '5851.0' '5851.5' '5873.0'
 '5875.5' '5875.5' '5875.5' '5915.0' '5915.0' '5937.0' '5937.0' '5938.0'
 '5938.0' '5938.0' '5916.0' '5903.0' '5888.0' '5888.0' '5893.5' '5893.5'
 '5893.5' '5869.44' '5909.44' '5937.22' '6025.0' '6026.5' '6026.5'
 '6026.5' '6049.0' '5879.7' '5879.7' '5825.7' '5825.7' '5825.7' '5825.7'
 '5775.0' '5692.0' '5675.0' '5675.0' '5675.0' '5675.0' '5675.0' '5681.5'
 '5681.5' '5681.5' '5694.0' '5745.0' '5745.0' '5745.0' '5728.5' '5728.5'
 '5657.5' '5657.5' '5666.5' '5666.5' '5666.5' '5712.5' '5727.5' '5727.5'
 '5727.5' '5874.0' '5874.0' '5874.0' '5874.0' '5910.0' '5900.0' '5953.0'
 '5930.0' '5930.0' '5930.0' '5930.0' '5942.5' '5896.0' '5896.0' '5893.5'
 '5893.5' '5893.5' '5893.5' '5766.0' '5723.5' '5718.0' '5718.0' '5731.0'
 '5731.0' '5753.0' '5794.0' '5796.0' '5824.0' '5854.0' '5854.0' '5854.0'
 '5884.9' '5884.9' '5902.0' '6007.0' '6007.0' '6007.0' '6007.0' '6007.0'
 '6001.0' '5994.0' '5994.0' '6020.0' '5971.11' '5950.0' '5950.0' '5945.0'
 '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0'
 '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5929.0' '5929.0' '5943.0'
 '5941.0' '5941.0' '5941.0' '5879.0' '5911.0' '5911.0' '5950.0' '5950.0'
 '5950.0' '5950.0' '5930.5' '5873.5' '5878.5' '5869.0' '5839.0' '5839.0'
 '5839.0' '5839.0' '5806.0' '5809.0' '5835.0' '5883.0' '5883.0' '5883.0'
 '5931.0' '5933.0' '5923.0' '5923.0' '5813.0' '5813.0' '5813.0' '5813.0'
 '5833.0' '5833.0' '5833.0' '5859.0' '5859.0' '5859.0' '5994.0' '5994.0'
 '6015.5' '6015.5' '6015.5' '6015.5' '6015.5' '5999.0' '5996.0' '5996.0'
 '5990.0' '5990.0' '5924.0' '5924.0' '5959.0' '5964.0' '5964.0' '5964.0'
 '5948.0' '5948.0' '5948.0' '5947.0' '5945.5' '5945.5' '5935.5' '5940.5'
 '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5'
 '5940.5' '5940.5' '5878.0' '5878.0' '5853.5' '5844.5' '5844.5' '5842.5'
 '5842.5' '5804.0' '5818.0' '5838.5' '5858.5' '5858.5' '5858.5' '5915.0'
 '5915.0' '5923.0' '5918.0' '5946.0' '5946.0' '5946.0' '5976.0' '5976.0'
 '5968.0' '6010.0' '6010.0' '6010.0' '6010.0' '5985.5' '5911.0' '5941.0'
 '5941.0' '5958.0' '5957.0' '5957.0' '5957.0' '5987.0' '5987.0' '5987.0'
 '5982.0' '5977.0' '5977.0' '5956.0' '5923.5' '5953.0' '5985.0' '5976.0'
 '5976.0' '5976.0' '5973.7' '5973.7' '6008.0' '6007.0' '6025.0' '6025.0'
 '6025.0' '6025.0' '6051.0' '6114.0' '6104.0' '6104.0' '6104.0' '6104.0'
 '6048.0' '5991.0' '5969.0' '5978.0' '5982.0' '5982.0' '5982.0' '5946.0'
 '5946.0' '5931.0' '5939.0' '5931.0' '5931.0' '5931.0' '5901.67' '5888.33'
 '5885.56' '5831.11' '5844.44' '5844.44' '5844.44' '5850.0' '5850.0'
 '5781.11' '5830.56' '5824.44' '5824.44' '5824.44' '5715.56' '5657.78'
 '5630.0' '5616.67' '5616.67' '5616.67' '5616.67' '5599.11' '5610.22'
 '5598.89' '5540.0' '5566.11' '5566.11' '5566.11' '5446.67' '5357.78'
 '5357.78' '5346.11' '5335.56' '5335.56' '5335.56' '5388.0' '5414.0'
 '5414.0' '5383.0' '5383.0' '5383.0' '5383.0' '5303.5' '5149.0' '5149.0'
 '5046.0' '5012.0' '5012.0' '5012.0' '4906.0' '4757.5' '4747.5' '4778.5'
 '4792.5' '4792.5' '4792.5' '4792.5' '4792.5' '4792.5' '4761.5' '4802.0'
 '4811.0' '4811.0' '4777.0' '4777.0' '4858.0' '4878.0' '4888.0' '4888.0'
 '4888.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0'
 '4928.0' '5271.0' '5218.5' '5206.5' '5206.5' '5208.5' '5208.5' '5208.5'
 '5079.0' '5009.0' '4887.0' '4912.0' '4912.0' '4912.0' '4904.0' '4904.0'
 '4956.0' '4955.0' '4953.0' '4953.0' '4953.0' '4927.0' '4881.0' '4881.0'
 '4901.0' '4900.0' '4900.0' '4900.0' '4900.0' '4900.0' '4825.0' '4877.0'
 '4915.0' '4915.0' '4915.0' '4915.0' '4915.0' '4841.0' '4807.0' '4803.0'
 '4803.0' '4803.0' '4795.56' '4792.22' '4824.44' '4828.89' '4832.22'
 '4832.22' '4832.22' '4832.22' '4764.0' '4783.0' '4741.0' '4741.0'
 '4741.0' '4741.0' '4741.0' '4754.7' '4759.7' '4759.7' '4759.7' '4759.7'
 '4759.7' '4680.5' '4697.0' '4697.0' '4734.0' '4734.0' '4734.0' '4734.0'
 '4734.0' '4734.0' '4797.0' '4809.0' '4809.0' '4809.0' '4809.0' '4832.0'
 '4832.0' '4833.0' '4831.0' '4767.0' '4767.0' '4767.0' '4767.0' '4790.0'
 '4790.0' '4790.0' '4790.0' '4795.56' '4795.56' '4795.56' '4795.56'
 '4803.89' '4871.67' '4893.89' '4893.89' '4893.89' '4963.89' '4996.11'
 '5084.44' '5084.44' '5084.44' '5084.44' '5084.44' '5081.11' '5081.11'
 '5068.89' '5048.89' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22'
 '4992.22' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22'
 '4954.0' '4956.82' '5023.27' '5023.27' '5023.27' '5118.18' '5115.91'
 '5115.91' '5077.73' '5084.09' '5084.09' '5084.09' '5104.09' '5123.18'
 '5121.82' '5122.73' '5091.82' '5091.82' '5091.82' '5036.36' '5032.73'
 '5032.73' '4967.27' '4967.27' '4967.27' '4967.27' '4993.64' '4998.18'
 '4881.82' '4881.82' '4876.55' '4876.55' '4876.55' '4850.91' '4796.36'
 '4748.18' '4748.18' '4797.45' '4797.45' '4797.45' '4816.91' '4855.64'
 '4846.36' '4848.82' '4877.27' '4877.27' '4877.27' '4877.27' '4889.8'
 '4911.4' '4912.9' '4906.6' '4906.6' '4906.6' '4861.6' '4910.0' '4919.0'
 '4868.0' '4868.0' '4868.0' '4868.0' '4722.5' '4672.5' '4518.5' '4489.0'
 '4440.5' '4338.12' '4338.12' '4368.75' '4328.75' '4253.75' '4318.75'
 '4320.62' '4320.62' '4320.62' '4330.0' '4316.25' '4366.88' '4360.0'
 '4493.75' '4493.75' '4493.75' '4567.5' '4565.0' '4584.38' '4584.38'
 '4584.38' '4584.38' '4584.38' '4582.5' '4529.17' '4555.84' '4688.34'
 '4750.84' '4750.84' '4750.84' '4888.33' '4913.34' '4913.34' '4991.66'
 '5007.5' '5007.5' '5007.5' '4993.75' '5001.25' '4986.25' '4913.34'
 '4915.0' '4915.0' '4915.0' '4917.5' '4938.75' '4893.75' '4972.5'
 '4965.62' '4965.62' '4965.62' '4965.62' '4948.75' '4896.5' '4880.66'
 '4903.25' '4903.25' '4903.25' '4884.5' '4880.5' '4871.75' '4880.75'
 '4932.75' '4932.75' '4932.75' '5008.25' '5045.0' '5165.5' '5246.5'
 '5271.5' '5271.5' '5271.5' '5282.0' '5114.0' '5114.0' '5083.25' '5084.75'
 '5084.75' '5084.75' '5104.75' '5081.66' '5031.25' '5031.25' '5031.25'
 '5031.25' '5031.25' '4977.5' '4930.84' '4852.5' '4852.5' '4852.5'
 '4852.5' '4852.5' '4789.84' '4735.66' '4735.66' '4762.66' '4776.66'
 '4776.66' '4776.66' '4825.0' '4817.0' '4831.34' '4831.34' '4874.84'
 '4874.84' '4874.84' '4862.5' '4863.34' '4889.16' '4859.59' '4821.25'
 '4821.25' '4821.25' '4808.75' '4765.0' '4771.25' '4771.25' '4742.5'
 '4742.5' '4742.5' '4750.0' '4731.88' '4731.88' '4717.5' '4726.88'
 '4726.88' '4726.88' '4720.25' '4726.0' '4727.0' '4771.0' '4771.0'
 '4771.0' '4771.0' '4855.0' '4922.5' '4913.75' '4910.0' '4891.25'
 '4891.25' '4891.25' '4755.5' '4759.5' '4749.5' '4740.5' '4739.5' '4739.5'
 '4739.5' '4720.0' '4643.5' '4649.16' '4649.16' '4636.66' '4636.66'
 '4636.66' '4631.66' '4633.75' '4649.0' '4644.0' '4631.25' '4631.25'
 '4631.25' '4626.25' '4618.75' '4572.91' '4635.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['19040.0' '19040.0' '19040.0' '18900.0' '18900.0' '18840.0' '18840.0'
 '18840.0' '18780.0' '18690.0' '18680.0' '18680.0' '18500.0' '18500.0'
 '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0'
 '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0'
 '18500.0' '18350.0' '18350.0' '18280.0' '18280.0' '17970.0' '17970.0'
 '17970.0' '17970.0' '17930.0' '17930.0' '17930.0' '17930.0' '17930.0'
 '17930.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0'
 '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0'
 '17960.0' '17960.0' '17960.0' '17890.0' '17890.0' '17860.0' '17860.0'
 '17860.0' '17830.0' '17830.0' '17740.0' '17740.0' '17720.0' '17720.0'
 '17720.0' '17660.0' '17610.0' '17490.0' '17400.0' '17300.0' '17300.0'
 '17300.0' '17230.0' '17150.0' '16940.0' '16890.0' '16890.0' '16890.0'
 '16890.0' '16890.0' '16890.0' '16890.0' '16890.0' '16890.0' '16890.0'
 '16890.0' '16890.0' '16680.0' '16680.0' '16510.0' '16420.0' '16420.0'
 '16420.0' '16400.0' '16290.0' '16250.0' '16250.0' '16250.0' '16250.0'
 '16250.0' '16250.0' '16250.0' '16250.0' '16250.0' '16250.0' '16250.0'
 '16160.0' '16140.0' '16140.0' '16140.0' '16140.0' '16140.0' '16140.0'
 '16140.0' '16140.0' '16140.0' '16140.0' '16140.0' '16110.0' '16110.0'
 '16110.0' '15980.0' '15820.0' '15790.0' '15680.0' '15680.0' '15680.0'
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 '14170.0' '14170.0' '14170.0' '14170.0' '14170.0' '14090.0' '14090.0'
 '14090.0' '14030.0' '13890.0' '13670.0' '13610.0' '13610.0' '13610.0'
 '13610.0' '13610.0' '13610.0' '13730.0' '13730.0' '13730.0' '13730.0'
 '13730.0' '13710.0' '13710.0' '13710.0' '13710.0' '13710.0' '13710.0'
 '13710.0' '13710.0' '13710.0' '13740.0' '13740.0' '13740.0' '13740.0'
 '13740.0' '13740.0' '13740.0' '13740.0' '13750.0' '13630.0' '13630.0'
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 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13660.0' '13690.0' '13880.0' '13880.0' '13880.0'
 '13880.0' '14000.0' '14000.0' '14000.0' '14050.0' '14050.0' '14050.0'
 '14050.0' '14130.0' '14160.0' '14180.0' '14340.0' '14340.0' '14410.0'
 '14410.0' '14460.0' '14460.0' '14460.0' '14460.0' '14460.0' '14460.0'
 '14460.0' '14510.0' '14670.0' '14760.0' '14890.0' '14890.0' '14890.0'
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 '15480.0' '15580.0' '15650.0' '15650.0' '15720.0' '15720.0' '15720.0'
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 '15840.0' '15840.0' '15840.0' '15840.0' '15840.0' '15840.0' '15840.0'
 '15840.0' '15840.0' '15840.0' '15830.0' '15820.0' '15800.0' '15800.0'
 '15800.0' '15770.0' '15750.0' '15680.0' '15650.0' '15650.0' '15650.0'
 '15650.0' '15650.0' '15650.0' '15650.0' '15550.0' '15500.0' '15500.0'
 '15500.0' '15500.0' '15500.0' '15480.0' '15480.0' '15420.0' '15420.0'
 '15420.0' '15420.0' '15300.0' '15220.0' '15190.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15210.0' '15210.0' '15210.0' '15210.0' '15210.0' '15210.0'
 '15210.0' '15220.0' '15220.0' '15260.0' '15260.0' '15260.0' '15260.0'
 '15260.0' '15280.0' '15330.0' '15330.0' '15480.0' '15480.0' '15560.0'
 '15560.0' '15620.0' '15620.0' '15630.0' '15620.0' '15620.0' '15620.0'
 '15620.0' '15620.0' '15620.0' '15620.0' '15620.0' '15620.0' '15620.0'
 '15620.0' '15610.0' '15610.0' '15560.0' '15560.0' '15560.0' '15560.0'
 '15560.0' '15500.0' '15470.0' '15460.0' '15400.0' '15390.0' '15390.0'
 '15390.0' '15380.0' '15340.0' '15340.0' '15340.0' '15310.0' '15310.0'
 '15310.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15280.0' '15240.0' '15180.0' '15140.0' '15140.0'
 '15140.0' '15130.0' '15130.0' '15070.0' '15000.0' '15000.0' '15000.0'
 '15000.0' '15000.0' '15000.0' '15000.0' '15000.0' '14940.0' '14940.0'
 '14940.0' '14940.0' '14940.0' '14900.0' '14830.0' '14730.0' '14730.0'
 '14730.0' '14690.0' '14590.0' '14550.0' '14540.0' '14470.0' '14470.0'
 '14470.0' '14360.0' '14270.0' '14180.0' '14090.0' '14070.0' '14070.0'
 '14070.0' '13990.0' '13960.0' '13860.0' '13860.0' '13860.0' '13860.0'
 '13860.0' '13740.0' '13690.0' '13680.0' '13680.0' '13680.0' '13680.0'
 '13680.0' '13680.0' '13680.0' '13680.0' '13680.0' '13680.0' '13680.0'
 '13680.0' '13680.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13610.0' '13570.0' '13570.0'
 '13570.0' '13570.0' '13520.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13470.0' '13470.0' '13470.0' '13470.0' '13470.0'
 '13470.0' '13450.0' '13380.0' '13360.0' '13360.0' '13360.0' '13360.0'
 '13360.0' '13340.0' '13250.0' '13230.0' '13230.0' '13230.0' '13230.0'
 '13230.0' '13210.0' '13110.0' '13070.0' '13070.0' '13030.0' '13030.0'
 '13030.0' '12960.0' '12930.0' '12930.0' '12880.0' '12880.0' '12880.0'
 '12880.0' '12820.0' '12760.0' '12680.0' '12590.0' '12560.0' '12560.0'
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 '12130.0' '12100.0' '12100.0' '12100.0' '12100.0' '12100.0' '12100.0'
 '12100.0' '12100.0' '12100.0' '12100.0' '12100.0' '12090.0' '12090.0'
 '12090.0' '12020.0' '11980.0' '11970.0' '11940.0' '11940.0' '11940.0'
 '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11940.0'
 '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11970.0' '11970.0' '11960.0' '11960.0' '11960.0'
 '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '11960.0'
 '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '12020.0' '12020.0'
 '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0'
 '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0'
 '12020.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12010.0' '12010.0' '11990.0' '11990.0' '11880.0' '11880.0'
 '11880.0' '11860.0' '11810.0' '11810.0' '11810.0' '11750.0' '11750.0'
 '11750.0' '11750.0' '11750.0' '11750.0' '11750.0' '11750.0' '11750.0'
 '11750.0' '11750.0' '11690.0' '11690.0' '11630.0' '11560.0' '11560.0'
 '11560.0' '11560.0' '11560.0' '11520.0' '11520.0' '11510.0' '11510.0'
 '11510.0' '11500.0' '11340.0' '11340.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11210.0' '11180.0' '11180.0' '11180.0'
 '11180.0' '11180.0' '11180.0' '11140.0' '11110.0' '11110.0' '11110.0'
 '11110.0' '11110.0' '11110.0' '11030.0' '10980.0' '10950.0' '10950.0'
 '10950.0' '10890.0' '10870.0' '10870.0' '10870.0' '10870.0' '10870.0'
 '10870.0' '10870.0' '10860.0' '10850.0' '10780.0' '10760.0' '10760.0'
 '10760.0' '10760.0' '10760.0' '10760.0' '10760.0' '10760.0' '10760.0'
 '10760.0' '10690.0' '10690.0' '10670.0' '10670.0' '10650.0' '10650.0'
 '10650.0' '10630.0' '10540.0' '10490.0' '10480.0' '10390.0' '10390.0'
 '10390.0' '10290.0' '10250.0' '10220.0' '10120.0' '10040.0' '10040.0'
 '10040.0' '10040.0' '9960.0' '9880.0' '9880.0' '9880.0' '9880.0' '9880.0'
 '9880.0' '9780.0' '9690.0' '9640.0' '9640.0' '9640.0' '9640.0' '9610.0'
 '9610.0' '9610.0' '9520.0' '9480.0' '9480.0' '9480.0' '9390.0' '9290.0'
 '9210.0' '9210.0' '9180.0' '9180.0' '9180.0' '9180.0' '9000.0' '8990.0'
 '8990.0' '8780.0' '8780.0' '8780.0' '8780.0' '8700.0' '8640.0' '8640.0'
 '8640.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0'
 '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0'
 '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8640.0' '8640.0'
 '8730.0' '8730.0' '8850.0' '8890.0' '8890.0' '8910.0' '8910.0' '8910.0'
 '8910.0' '8910.0' '8920.0' '8940.0' '9050.0' '9050.0' '9090.0' '9330.0'
 '9380.0' '9380.0' '9500.0' '9500.0' '9500.0' '9620.0' '9840.0' '10080.0'
 '10180.0' '10180.0' '10180.0' '10180.0' '10130.0' '9970.0' '10110.0'
 '10000.0' '9980.0' '9980.0' '9980.0' '9780.0' '9590.0' '9590.0' '9590.0'
 '9590.0' '9590.0' '9590.0' '9630.0' '9630.0' '9630.0' '9630.0' '9610.0'
 '9610.0' '9610.0' '9610.0' '9610.0' '9530.0' '9530.0' '9530.0' '9530.0'
 '9530.0' '9570.0' '9570.0' '9570.0' '9540.0' '9360.0' '9360.0' '9360.0'
 '9360.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0'
 '9390.0' '9390.0' '9390.0' '9450.0' '9450.0' '9450.0' '9450.0' '9560.0'
 '9580.0' '9600.0' '9600.0' '9600.0' '9600.0' '9700.0' '9700.0' '9700.0'
 '9700.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['19040.0' '19040.0' '19040.0' '18900.0' '18900.0' '18840.0' '18840.0'
 '18840.0' '18780.0' '18690.0' '18680.0' '18680.0' '18500.0' '18500.0'
 '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0'
 '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0'
 '18500.0' '18350.0' '18350.0' '18280.0' '18280.0' '17970.0' '17970.0'
 '17970.0' '17970.0' '17930.0' '17930.0' '17930.0' '17930.0' '17930.0'
 '17930.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0'
 '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0'
 '17960.0' '17960.0' '17960.0' '17890.0' '17890.0' '17860.0' '17860.0'
 '17860.0' '17830.0' '17830.0' '17740.0' '17740.0' '17720.0' '17720.0'
 '17720.0' '17660.0' '17610.0' '17490.0' '17400.0' '17300.0' '17300.0'
 '17300.0' '17230.0' '17150.0' '16940.0' '16890.0' '16890.0' '16890.0'
 '16890.0' '16890.0' '16890.0' '16890.0' '16890.0' '16890.0' '16890.0'
 '16890.0' '16890.0' '16680.0' '16680.0' '16510.0' '16420.0' '16420.0'
 '16420.0' '16400.0' '16290.0' '16250.0' '16250.0' '16250.0' '16250.0'
 '16250.0' '16250.0' '16250.0' '16250.0' '16250.0' '16250.0' '16250.0'
 '16160.0' '16140.0' '16140.0' '16140.0' '16140.0' '16140.0' '16140.0'
 '16140.0' '16140.0' '16140.0' '16140.0' '16140.0' '16110.0' '16110.0'
 '16110.0' '15980.0' '15820.0' '15790.0' '15680.0' '15680.0' '15680.0'
 '15680.0' '15460.0' '15340.0' '15210.0' '15040.0' '14870.0' '14870.0'
 '14870.0' '14670.0' '14480.0' '14360.0' '14270.0' '14210.0' '14210.0'
 '14210.0' '14170.0' '14170.0' '14170.0' '14170.0' '14170.0' '14170.0'
 '14170.0' '14170.0' '14170.0' '14170.0' '14170.0' '14090.0' '14090.0'
 '14090.0' '14030.0' '13890.0' '13670.0' '13610.0' '13610.0' '13610.0'
 '13610.0' '13610.0' '13610.0' '13730.0' '13730.0' '13730.0' '13730.0'
 '13730.0' '13710.0' '13710.0' '13710.0' '13710.0' '13710.0' '13710.0'
 '13710.0' '13710.0' '13710.0' '13740.0' '13740.0' '13740.0' '13740.0'
 '13740.0' '13740.0' '13740.0' '13740.0' '13750.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13660.0' '13690.0' '13880.0' '13880.0' '13880.0'
 '13880.0' '14000.0' '14000.0' '14000.0' '14050.0' '14050.0' '14050.0'
 '14050.0' '14130.0' '14160.0' '14180.0' '14340.0' '14340.0' '14410.0'
 '14410.0' '14460.0' '14460.0' '14460.0' '14460.0' '14460.0' '14460.0'
 '14460.0' '14510.0' '14670.0' '14760.0' '14890.0' '14890.0' '14890.0'
 '14890.0' '14900.0' '15020.0' '15270.0' '15380.0' '15480.0' '15480.0'
 '15480.0' '15580.0' '15650.0' '15650.0' '15720.0' '15720.0' '15720.0'
 '15720.0' '15820.0' '15820.0' '15840.0' '15840.0' '15840.0' '15840.0'
 '15840.0' '15840.0' '15840.0' '15840.0' '15840.0' '15840.0' '15840.0'
 '15840.0' '15840.0' '15840.0' '15830.0' '15820.0' '15800.0' '15800.0'
 '15800.0' '15770.0' '15750.0' '15680.0' '15650.0' '15650.0' '15650.0'
 '15650.0' '15650.0' '15650.0' '15650.0' '15550.0' '15500.0' '15500.0'
 '15500.0' '15500.0' '15500.0' '15480.0' '15480.0' '15420.0' '15420.0'
 '15420.0' '15420.0' '15300.0' '15220.0' '15190.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15210.0' '15210.0' '15210.0' '15210.0' '15210.0' '15210.0'
 '15210.0' '15220.0' '15220.0' '15260.0' '15260.0' '15260.0' '15260.0'
 '15260.0' '15280.0' '15330.0' '15330.0' '15480.0' '15480.0' '15560.0'
 '15560.0' '15620.0' '15620.0' '15630.0' '15620.0' '15620.0' '15620.0'
 '15620.0' '15620.0' '15620.0' '15620.0' '15620.0' '15620.0' '15620.0'
 '15620.0' '15610.0' '15610.0' '15560.0' '15560.0' '15560.0' '15560.0'
 '15560.0' '15500.0' '15470.0' '15460.0' '15400.0' '15390.0' '15390.0'
 '15390.0' '15380.0' '15340.0' '15340.0' '15340.0' '15310.0' '15310.0'
 '15310.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15280.0' '15240.0' '15180.0' '15140.0' '15140.0'
 '15140.0' '15130.0' '15130.0' '15070.0' '15000.0' '15000.0' '15000.0'
 '15000.0' '15000.0' '15000.0' '15000.0' '15000.0' '14940.0' '14940.0'
 '14940.0' '14940.0' '14940.0' '14900.0' '14830.0' '14730.0' '14730.0'
 '14730.0' '14690.0' '14590.0' '14550.0' '14540.0' '14470.0' '14470.0'
 '14470.0' '14360.0' '14270.0' '14180.0' '14090.0' '14070.0' '14070.0'
 '14070.0' '13990.0' '13960.0' '13860.0' '13860.0' '13860.0' '13860.0'
 '13860.0' '13740.0' '13690.0' '13680.0' '13680.0' '13680.0' '13680.0'
 '13680.0' '13680.0' '13680.0' '13680.0' '13680.0' '13680.0' '13680.0'
 '13680.0' '13680.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13610.0' '13570.0' '13570.0'
 '13570.0' '13570.0' '13520.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13470.0' '13470.0' '13470.0' '13470.0' '13470.0'
 '13470.0' '13450.0' '13380.0' '13360.0' '13360.0' '13360.0' '13360.0'
 '13360.0' '13340.0' '13250.0' '13230.0' '13230.0' '13230.0' '13230.0'
 '13230.0' '13210.0' '13110.0' '13070.0' '13070.0' '13030.0' '13030.0'
 '13030.0' '12960.0' '12930.0' '12930.0' '12880.0' '12880.0' '12880.0'
 '12880.0' '12820.0' '12760.0' '12680.0' '12590.0' '12560.0' '12560.0'
 '12560.0' '12520.0' '12410.0' '12410.0' '12220.0' '12130.0' '12130.0'
 '12130.0' '12100.0' '12100.0' '12100.0' '12100.0' '12100.0' '12100.0'
 '12100.0' '12100.0' '12100.0' '12100.0' '12100.0' '12090.0' '12090.0'
 '12090.0' '12020.0' '11980.0' '11970.0' '11940.0' '11940.0' '11940.0'
 '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11940.0'
 '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11970.0' '11970.0' '11960.0' '11960.0' '11960.0'
 '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '11960.0'
 '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '12020.0' '12020.0'
 '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0'
 '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0'
 '12020.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12010.0' '12010.0' '11990.0' '11990.0' '11880.0' '11880.0'
 '11880.0' '11860.0' '11810.0' '11810.0' '11810.0' '11750.0' '11750.0'
 '11750.0' '11750.0' '11750.0' '11750.0' '11750.0' '11750.0' '11750.0'
 '11750.0' '11750.0' '11690.0' '11690.0' '11630.0' '11560.0' '11560.0'
 '11560.0' '11560.0' '11560.0' '11520.0' '11520.0' '11510.0' '11510.0'
 '11510.0' '11500.0' '11340.0' '11340.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11210.0' '11180.0' '11180.0' '11180.0'
 '11180.0' '11180.0' '11180.0' '11140.0' '11110.0' '11110.0' '11110.0'
 '11110.0' '11110.0' '11110.0' '11030.0' '10980.0' '10950.0' '10950.0'
 '10950.0' '10890.0' '10870.0' '10870.0' '10870.0' '10870.0' '10870.0'
 '10870.0' '10870.0' '10860.0' '10850.0' '10780.0' '10760.0' '10760.0'
 '10760.0' '10760.0' '10760.0' '10760.0' '10760.0' '10760.0' '10760.0'
 '10760.0' '10690.0' '10690.0' '10670.0' '10670.0' '10650.0' '10650.0'
 '10650.0' '10630.0' '10540.0' '10490.0' '10480.0' '10390.0' '10390.0'
 '10390.0' '10290.0' '10250.0' '10220.0' '10120.0' '10040.0' '10040.0'
 '10040.0' '10040.0' '9960.0' '9880.0' '9880.0' '9880.0' '9880.0' '9880.0'
 '9880.0' '9780.0' '9690.0' '9640.0' '9640.0' '9640.0' '9640.0' '9610.0'
 '9610.0' '9610.0' '9520.0' '9480.0' '9480.0' '9480.0' '9390.0' '9290.0'
 '9210.0' '9210.0' '9180.0' '9180.0' '9180.0' '9180.0' '9000.0' '8990.0'
 '8990.0' '8780.0' '8780.0' '8780.0' '8780.0' '8700.0' '8640.0' '8640.0'
 '8640.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0'
 '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0'
 '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8640.0' '8640.0'
 '8730.0' '8730.0' '8850.0' '8890.0' '8890.0' '8910.0' '8910.0' '8910.0'
 '8910.0' '8910.0' '8920.0' '8940.0' '9050.0' '9050.0' '9090.0' '9330.0'
 '9380.0' '9380.0' '9500.0' '9500.0' '9500.0' '9620.0' '9840.0' '10080.0'
 '10180.0' '10180.0' '10180.0' '10180.0' '10130.0' '9970.0' '10110.0'
 '10000.0' '9980.0' '9980.0' '9980.0' '9780.0' '9590.0' '9590.0' '9590.0'
 '9590.0' '9590.0' '9590.0' '9630.0' '9630.0' '9630.0' '9630.0' '9610.0'
 '9610.0' '9610.0' '9610.0' '9610.0' '9530.0' '9530.0' '9530.0' '9530.0'
 '9530.0' '9570.0' '9570.0' '9570.0' '9540.0' '9360.0' '9360.0' '9360.0'
 '9360.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0'
 '9390.0' '9390.0' '9390.0' '9450.0' '9450.0' '9450.0' '9450.0' '9560.0'
 '9580.0' '9600.0' '9600.0' '9600.0' '9600.0' '9700.0' '9700.0' '9700.0'
 '9700.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5616.36' '5616.36' '5609.09' '5510.0' '5395.0' '5405.91' '5405.91'
 '5405.91' '5377.73' '5376.36' '5376.36' '5376.36' '5535.45' '5535.45'
 '5535.45' '5535.45' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27'
 '5537.27' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27'
 '5537.27' '5779.55' '5748.18' '5763.64' '5763.64' '5763.64' '5763.64'
 '5763.64' '5617.73' '5617.73' '5617.73' '5618.18' '5618.18' '5618.18'
 '5618.18' '5480.0' '5481.82' '5481.82' '5522.73' '5522.73' '5522.73'
 '5522.73' '5496.36' '5532.73' '5619.09' '5619.09' '5645.45' '5645.45'
 '5645.45' '5597.27' '5597.27' '5597.27' '5726.0' '5771.45' '5771.45'
 '5771.45' '5790.0' '5790.0' '5790.0' '5872.55' '5868.0' '5868.0' '5868.0'
 '5897.09' '5750.0' '5732.27' '5698.18' '5827.27' '5827.27' '5827.27'
 '5914.55' '5987.27' '6009.09' '6063.64' '6167.27' '6167.27' '6167.27'
 '6214.91' '6294.91' '6294.91' '6358.18' '6404.55' '6404.55' '6404.55'
 '6465.45' '6486.36' '6486.36' '6454.55' '6454.55' '6454.55' '6454.55'
 '6350.91' '6360.0' '6230.91' '6246.36' '6370.91' '6370.91' '6370.91'
 '6520.91' '6493.64' '6488.64' '6419.09' '6390.91' '6390.91' '6390.91'
 '6321.82' '6280.91' '6259.09' '6180.0' '6180.0' '6180.0' '6180.0'
 '6180.0' '6180.0' '6180.0' '6079.09' '5984.55' '5993.64' '5993.64'
 '6013.64' '6013.64' '6013.64' '6013.64' '5680.0' '5680.0' '5680.0'
 '5680.0' '5529.09' '5529.09' '5539.09' '5590.0' '5590.0' '5590.0'
 '5560.36' '5589.45' '5589.45' '5599.09' '5590.91' '5590.91' '5590.91'
 '5614.55' '5614.55' '5614.55' '5619.09' '5670.0' '5670.0' '5670.0'
 '5670.0' '5684.55' '5676.36' '5661.82' '5661.82' '5661.82' '5661.82'
 '5596.36' '5626.36' '5706.36' '5737.27' '5737.27' '5737.27' '5737.27'
 '5685.45' '5675.45' '5695.45' '5695.45' '5695.45' '5695.45' '5690.0'
 '5594.55' '5631.82' '5631.82' '5600.0' '5600.0' '5600.0' '5600.0'
 '5650.91' '5678.55' '5678.55' '5678.55' '5745.82' '5745.82' '5745.82'
 '5745.82' '5814.0' '5910.0' '5910.0' '5910.0' '5980.91' '5980.91'
 '5909.09' '5882.73' '5899.09' '5899.09' '5910.91' '5910.91' '5910.91'
 '5898.64' '5898.64' '5916.82' '5950.0' '5986.36' '6000.91' '6000.91'
 '6001.82' '6036.36' '6036.36' '5880.91' '5872.73' '5872.73' '5872.73'
 '5877.27' '5848.18' '5870.0' '5879.09' '5914.55' '5914.55' '5914.55'
 '5914.55' '5910.0' '5875.0' '5874.55' '5872.73' '5896.36' '5896.36'
 '6019.09' '6067.27' '6074.09' '6062.27' '6081.36' '6081.36' '6081.36'
 '6081.36' '6080.91' '6164.55' '6180.91' '6200.0' '6200.0' '6200.0'
 '6188.18' '6173.64' '6200.91' '6188.18' '6188.18' '6198.0' '6198.0'
 '6200.0' '6342.0' '6329.0' '6386.0' '6439.0' '6439.0' '6439.0' '6410.0'
 '6389.0' '6346.0' '6300.0' '6300.0' '6289.0' '6289.0' '6276.82' '6230.45'
 '6204.55' '6213.64' '6213.64' '6213.64' '6213.64' '6213.64' '6213.64'
 '6213.64' '6213.64' '6213.64' '6213.64' '6213.64' '6013.64' '5964.55'
 '5961.82' '5898.0' '5854.5' '5854.5' '5854.5' '5871.0' '5870.0' '5867.5'
 '5856.0' '5851.0' '5851.0' '5851.0' '5851.0' '5851.0' '5851.5' '5873.0'
 '5875.5' '5875.5' '5875.5' '5915.0' '5915.0' '5937.0' '5937.0' '5938.0'
 '5938.0' '5938.0' '5916.0' '5903.0' '5888.0' '5888.0' '5893.5' '5893.5'
 '5893.5' '5869.44' '5909.44' '5937.22' '6025.0' '6026.5' '6026.5'
 '6026.5' '6049.0' '5879.7' '5879.7' '5825.7' '5825.7' '5825.7' '5825.7'
 '5775.0' '5692.0' '5675.0' '5675.0' '5675.0' '5675.0' '5675.0' '5681.5'
 '5681.5' '5681.5' '5694.0' '5745.0' '5745.0' '5745.0' '5728.5' '5728.5'
 '5657.5' '5657.5' '5666.5' '5666.5' '5666.5' '5712.5' '5727.5' '5727.5'
 '5727.5' '5874.0' '5874.0' '5874.0' '5874.0' '5910.0' '5900.0' '5953.0'
 '5930.0' '5930.0' '5930.0' '5930.0' '5942.5' '5896.0' '5896.0' '5893.5'
 '5893.5' '5893.5' '5893.5' '5766.0' '5723.5' '5718.0' '5718.0' '5731.0'
 '5731.0' '5753.0' '5794.0' '5796.0' '5824.0' '5854.0' '5854.0' '5854.0'
 '5884.9' '5884.9' '5902.0' '6007.0' '6007.0' '6007.0' '6007.0' '6007.0'
 '6001.0' '5994.0' '5994.0' '6020.0' '5971.11' '5950.0' '5950.0' '5945.0'
 '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0'
 '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5929.0' '5929.0' '5943.0'
 '5941.0' '5941.0' '5941.0' '5879.0' '5911.0' '5911.0' '5950.0' '5950.0'
 '5950.0' '5950.0' '5930.5' '5873.5' '5878.5' '5869.0' '5839.0' '5839.0'
 '5839.0' '5839.0' '5806.0' '5809.0' '5835.0' '5883.0' '5883.0' '5883.0'
 '5931.0' '5933.0' '5923.0' '5923.0' '5813.0' '5813.0' '5813.0' '5813.0'
 '5833.0' '5833.0' '5833.0' '5859.0' '5859.0' '5859.0' '5994.0' '5994.0'
 '6015.5' '6015.5' '6015.5' '6015.5' '6015.5' '5999.0' '5996.0' '5996.0'
 '5990.0' '5990.0' '5924.0' '5924.0' '5959.0' '5964.0' '5964.0' '5964.0'
 '5948.0' '5948.0' '5948.0' '5947.0' '5945.5' '5945.5' '5935.5' '5940.5'
 '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5'
 '5940.5' '5940.5' '5878.0' '5878.0' '5853.5' '5844.5' '5844.5' '5842.5'
 '5842.5' '5804.0' '5818.0' '5838.5' '5858.5' '5858.5' '5858.5' '5915.0'
 '5915.0' '5923.0' '5918.0' '5946.0' '5946.0' '5946.0' '5976.0' '5976.0'
 '5968.0' '6010.0' '6010.0' '6010.0' '6010.0' '5985.5' '5911.0' '5941.0'
 '5941.0' '5958.0' '5957.0' '5957.0' '5957.0' '5987.0' '5987.0' '5987.0'
 '5982.0' '5977.0' '5977.0' '5956.0' '5923.5' '5953.0' '5985.0' '5976.0'
 '5976.0' '5976.0' '5973.7' '5973.7' '6008.0' '6007.0' '6025.0' '6025.0'
 '6025.0' '6025.0' '6051.0' '6114.0' '6104.0' '6104.0' '6104.0' '6104.0'
 '6048.0' '5991.0' '5969.0' '5978.0' '5982.0' '5982.0' '5982.0' '5946.0'
 '5946.0' '5931.0' '5939.0' '5931.0' '5931.0' '5931.0' '5901.67' '5888.33'
 '5885.56' '5831.11' '5844.44' '5844.44' '5844.44' '5850.0' '5850.0'
 '5781.11' '5830.56' '5824.44' '5824.44' '5824.44' '5715.56' '5657.78'
 '5630.0' '5616.67' '5616.67' '5616.67' '5616.67' '5599.11' '5610.22'
 '5598.89' '5540.0' '5566.11' '5566.11' '5566.11' '5446.67' '5357.78'
 '5357.78' '5346.11' '5335.56' '5335.56' '5335.56' '5388.0' '5414.0'
 '5414.0' '5383.0' '5383.0' '5383.0' '5383.0' '5303.5' '5149.0' '5149.0'
 '5046.0' '5012.0' '5012.0' '5012.0' '4906.0' '4757.5' '4747.5' '4778.5'
 '4792.5' '4792.5' '4792.5' '4792.5' '4792.5' '4792.5' '4761.5' '4802.0'
 '4811.0' '4811.0' '4777.0' '4777.0' '4858.0' '4878.0' '4888.0' '4888.0'
 '4888.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0'
 '4928.0' '5271.0' '5218.5' '5206.5' '5206.5' '5208.5' '5208.5' '5208.5'
 '5079.0' '5009.0' '4887.0' '4912.0' '4912.0' '4912.0' '4904.0' '4904.0'
 '4956.0' '4955.0' '4953.0' '4953.0' '4953.0' '4927.0' '4881.0' '4881.0'
 '4901.0' '4900.0' '4900.0' '4900.0' '4900.0' '4900.0' '4825.0' '4877.0'
 '4915.0' '4915.0' '4915.0' '4915.0' '4915.0' '4841.0' '4807.0' '4803.0'
 '4803.0' '4803.0' '4795.56' '4792.22' '4824.44' '4828.89' '4832.22'
 '4832.22' '4832.22' '4832.22' '4764.0' '4783.0' '4741.0' '4741.0'
 '4741.0' '4741.0' '4741.0' '4754.7' '4759.7' '4759.7' '4759.7' '4759.7'
 '4759.7' '4680.5' '4697.0' '4697.0' '4734.0' '4734.0' '4734.0' '4734.0'
 '4734.0' '4734.0' '4797.0' '4809.0' '4809.0' '4809.0' '4809.0' '4832.0'
 '4832.0' '4833.0' '4831.0' '4767.0' '4767.0' '4767.0' '4767.0' '4790.0'
 '4790.0' '4790.0' '4790.0' '4795.56' '4795.56' '4795.56' '4795.56'
 '4803.89' '4871.67' '4893.89' '4893.89' '4893.89' '4963.89' '4996.11'
 '5084.44' '5084.44' '5084.44' '5084.44' '5084.44' '5081.11' '5081.11'
 '5068.89' '5048.89' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22'
 '4992.22' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22'
 '4954.0' '4956.82' '5023.27' '5023.27' '5023.27' '5118.18' '5115.91'
 '5115.91' '5077.73' '5084.09' '5084.09' '5084.09' '5104.09' '5123.18'
 '5121.82' '5122.73' '5091.82' '5091.82' '5091.82' '5036.36' '5032.73'
 '5032.73' '4967.27' '4967.27' '4967.27' '4967.27' '4993.64' '4998.18'
 '4881.82' '4881.82' '4876.55' '4876.55' '4876.55' '4850.91' '4796.36'
 '4748.18' '4748.18' '4797.45' '4797.45' '4797.45' '4816.91' '4855.64'
 '4846.36' '4848.82' '4877.27' '4877.27' '4877.27' '4877.27' '4889.8'
 '4911.4' '4912.9' '4906.6' '4906.6' '4906.6' '4861.6' '4910.0' '4919.0'
 '4868.0' '4868.0' '4868.0' '4868.0' '4722.5' '4672.5' '4518.5' '4489.0'
 '4440.5' '4338.12' '4338.12' '4368.75' '4328.75' '4253.75' '4318.75'
 '4320.62' '4320.62' '4320.62' '4330.0' '4316.25' '4366.88' '4360.0'
 '4493.75' '4493.75' '4493.75' '4567.5' '4565.0' '4584.38' '4584.38'
 '4584.38' '4584.38' '4584.38' '4582.5' '4529.17' '4555.84' '4688.34'
 '4750.84' '4750.84' '4750.84' '4888.33' '4913.34' '4913.34' '4991.66'
 '5007.5' '5007.5' '5007.5' '4993.75' '5001.25' '4986.25' '4913.34'
 '4915.0' '4915.0' '4915.0' '4917.5' '4938.75' '4893.75' '4972.5'
 '4965.62' '4965.62' '4965.62' '4965.62' '4948.75' '4896.5' '4880.66'
 '4903.25' '4903.25' '4903.25' '4884.5' '4880.5' '4871.75' '4880.75'
 '4932.75' '4932.75' '4932.75' '5008.25' '5045.0' '5165.5' '5246.5'
 '5271.5' '5271.5' '5271.5' '5282.0' '5114.0' '5114.0' '5083.25' '5084.75'
 '5084.75' '5084.75' '5104.75' '5081.66' '5031.25' '5031.25' '5031.25'
 '5031.25' '5031.25' '4977.5' '4930.84' '4852.5' '4852.5' '4852.5'
 '4852.5' '4852.5' '4789.84' '4735.66' '4735.66' '4762.66' '4776.66'
 '4776.66' '4776.66' '4825.0' '4817.0' '4831.34' '4831.34' '4874.84'
 '4874.84' '4874.84' '4862.5' '4863.34' '4889.16' '4859.59' '4821.25'
 '4821.25' '4821.25' '4808.75' '4765.0' '4771.25' '4771.25' '4742.5'
 '4742.5' '4742.5' '4750.0' '4731.88' '4731.88' '4717.5' '4726.88'
 '4726.88' '4726.88' '4720.25' '4726.0' '4727.0' '4771.0' '4771.0'
 '4771.0' '4771.0' '4855.0' '4922.5' '4913.75' '4910.0' '4891.25'
 '4891.25' '4891.25' '4755.5' '4759.5' '4749.5' '4740.5' '4739.5' '4739.5'
 '4739.5' '4720.0' '4643.5' '4649.16' '4649.16' '4636.66' '4636.66'
 '4636.66' '4631.66' '4633.75' '4649.0' '4644.0' '4631.25' '4631.25'
 '4631.25' '4626.25' '4618.75' '4572.91' '4635.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['5616.36' '5616.36' '5609.09' '5510.0' '5395.0' '5405.91' '5405.91'
 '5405.91' '5377.73' '5376.36' '5376.36' '5376.36' '5535.45' '5535.45'
 '5535.45' '5535.45' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27'
 '5537.27' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27' '5537.27'
 '5537.27' '5779.55' '5748.18' '5763.64' '5763.64' '5763.64' '5763.64'
 '5763.64' '5617.73' '5617.73' '5617.73' '5618.18' '5618.18' '5618.18'
 '5618.18' '5480.0' '5481.82' '5481.82' '5522.73' '5522.73' '5522.73'
 '5522.73' '5496.36' '5532.73' '5619.09' '5619.09' '5645.45' '5645.45'
 '5645.45' '5597.27' '5597.27' '5597.27' '5726.0' '5771.45' '5771.45'
 '5771.45' '5790.0' '5790.0' '5790.0' '5872.55' '5868.0' '5868.0' '5868.0'
 '5897.09' '5750.0' '5732.27' '5698.18' '5827.27' '5827.27' '5827.27'
 '5914.55' '5987.27' '6009.09' '6063.64' '6167.27' '6167.27' '6167.27'
 '6214.91' '6294.91' '6294.91' '6358.18' '6404.55' '6404.55' '6404.55'
 '6465.45' '6486.36' '6486.36' '6454.55' '6454.55' '6454.55' '6454.55'
 '6350.91' '6360.0' '6230.91' '6246.36' '6370.91' '6370.91' '6370.91'
 '6520.91' '6493.64' '6488.64' '6419.09' '6390.91' '6390.91' '6390.91'
 '6321.82' '6280.91' '6259.09' '6180.0' '6180.0' '6180.0' '6180.0'
 '6180.0' '6180.0' '6180.0' '6079.09' '5984.55' '5993.64' '5993.64'
 '6013.64' '6013.64' '6013.64' '6013.64' '5680.0' '5680.0' '5680.0'
 '5680.0' '5529.09' '5529.09' '5539.09' '5590.0' '5590.0' '5590.0'
 '5560.36' '5589.45' '5589.45' '5599.09' '5590.91' '5590.91' '5590.91'
 '5614.55' '5614.55' '5614.55' '5619.09' '5670.0' '5670.0' '5670.0'
 '5670.0' '5684.55' '5676.36' '5661.82' '5661.82' '5661.82' '5661.82'
 '5596.36' '5626.36' '5706.36' '5737.27' '5737.27' '5737.27' '5737.27'
 '5685.45' '5675.45' '5695.45' '5695.45' '5695.45' '5695.45' '5690.0'
 '5594.55' '5631.82' '5631.82' '5600.0' '5600.0' '5600.0' '5600.0'
 '5650.91' '5678.55' '5678.55' '5678.55' '5745.82' '5745.82' '5745.82'
 '5745.82' '5814.0' '5910.0' '5910.0' '5910.0' '5980.91' '5980.91'
 '5909.09' '5882.73' '5899.09' '5899.09' '5910.91' '5910.91' '5910.91'
 '5898.64' '5898.64' '5916.82' '5950.0' '5986.36' '6000.91' '6000.91'
 '6001.82' '6036.36' '6036.36' '5880.91' '5872.73' '5872.73' '5872.73'
 '5877.27' '5848.18' '5870.0' '5879.09' '5914.55' '5914.55' '5914.55'
 '5914.55' '5910.0' '5875.0' '5874.55' '5872.73' '5896.36' '5896.36'
 '6019.09' '6067.27' '6074.09' '6062.27' '6081.36' '6081.36' '6081.36'
 '6081.36' '6080.91' '6164.55' '6180.91' '6200.0' '6200.0' '6200.0'
 '6188.18' '6173.64' '6200.91' '6188.18' '6188.18' '6198.0' '6198.0'
 '6200.0' '6342.0' '6329.0' '6386.0' '6439.0' '6439.0' '6439.0' '6410.0'
 '6389.0' '6346.0' '6300.0' '6300.0' '6289.0' '6289.0' '6276.82' '6230.45'
 '6204.55' '6213.64' '6213.64' '6213.64' '6213.64' '6213.64' '6213.64'
 '6213.64' '6213.64' '6213.64' '6213.64' '6213.64' '6013.64' '5964.55'
 '5961.82' '5898.0' '5854.5' '5854.5' '5854.5' '5871.0' '5870.0' '5867.5'
 '5856.0' '5851.0' '5851.0' '5851.0' '5851.0' '5851.0' '5851.5' '5873.0'
 '5875.5' '5875.5' '5875.5' '5915.0' '5915.0' '5937.0' '5937.0' '5938.0'
 '5938.0' '5938.0' '5916.0' '5903.0' '5888.0' '5888.0' '5893.5' '5893.5'
 '5893.5' '5869.44' '5909.44' '5937.22' '6025.0' '6026.5' '6026.5'
 '6026.5' '6049.0' '5879.7' '5879.7' '5825.7' '5825.7' '5825.7' '5825.7'
 '5775.0' '5692.0' '5675.0' '5675.0' '5675.0' '5675.0' '5675.0' '5681.5'
 '5681.5' '5681.5' '5694.0' '5745.0' '5745.0' '5745.0' '5728.5' '5728.5'
 '5657.5' '5657.5' '5666.5' '5666.5' '5666.5' '5712.5' '5727.5' '5727.5'
 '5727.5' '5874.0' '5874.0' '5874.0' '5874.0' '5910.0' '5900.0' '5953.0'
 '5930.0' '5930.0' '5930.0' '5930.0' '5942.5' '5896.0' '5896.0' '5893.5'
 '5893.5' '5893.5' '5893.5' '5766.0' '5723.5' '5718.0' '5718.0' '5731.0'
 '5731.0' '5753.0' '5794.0' '5796.0' '5824.0' '5854.0' '5854.0' '5854.0'
 '5884.9' '5884.9' '5902.0' '6007.0' '6007.0' '6007.0' '6007.0' '6007.0'
 '6001.0' '5994.0' '5994.0' '6020.0' '5971.11' '5950.0' '5950.0' '5945.0'
 '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5945.0'
 '5945.0' '5945.0' '5945.0' '5945.0' '5945.0' '5929.0' '5929.0' '5943.0'
 '5941.0' '5941.0' '5941.0' '5879.0' '5911.0' '5911.0' '5950.0' '5950.0'
 '5950.0' '5950.0' '5930.5' '5873.5' '5878.5' '5869.0' '5839.0' '5839.0'
 '5839.0' '5839.0' '5806.0' '5809.0' '5835.0' '5883.0' '5883.0' '5883.0'
 '5931.0' '5933.0' '5923.0' '5923.0' '5813.0' '5813.0' '5813.0' '5813.0'
 '5833.0' '5833.0' '5833.0' '5859.0' '5859.0' '5859.0' '5994.0' '5994.0'
 '6015.5' '6015.5' '6015.5' '6015.5' '6015.5' '5999.0' '5996.0' '5996.0'
 '5990.0' '5990.0' '5924.0' '5924.0' '5959.0' '5964.0' '5964.0' '5964.0'
 '5948.0' '5948.0' '5948.0' '5947.0' '5945.5' '5945.5' '5935.5' '5940.5'
 '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5' '5940.5'
 '5940.5' '5940.5' '5878.0' '5878.0' '5853.5' '5844.5' '5844.5' '5842.5'
 '5842.5' '5804.0' '5818.0' '5838.5' '5858.5' '5858.5' '5858.5' '5915.0'
 '5915.0' '5923.0' '5918.0' '5946.0' '5946.0' '5946.0' '5976.0' '5976.0'
 '5968.0' '6010.0' '6010.0' '6010.0' '6010.0' '5985.5' '5911.0' '5941.0'
 '5941.0' '5958.0' '5957.0' '5957.0' '5957.0' '5987.0' '5987.0' '5987.0'
 '5982.0' '5977.0' '5977.0' '5956.0' '5923.5' '5953.0' '5985.0' '5976.0'
 '5976.0' '5976.0' '5973.7' '5973.7' '6008.0' '6007.0' '6025.0' '6025.0'
 '6025.0' '6025.0' '6051.0' '6114.0' '6104.0' '6104.0' '6104.0' '6104.0'
 '6048.0' '5991.0' '5969.0' '5978.0' '5982.0' '5982.0' '5982.0' '5946.0'
 '5946.0' '5931.0' '5939.0' '5931.0' '5931.0' '5931.0' '5901.67' '5888.33'
 '5885.56' '5831.11' '5844.44' '5844.44' '5844.44' '5850.0' '5850.0'
 '5781.11' '5830.56' '5824.44' '5824.44' '5824.44' '5715.56' '5657.78'
 '5630.0' '5616.67' '5616.67' '5616.67' '5616.67' '5599.11' '5610.22'
 '5598.89' '5540.0' '5566.11' '5566.11' '5566.11' '5446.67' '5357.78'
 '5357.78' '5346.11' '5335.56' '5335.56' '5335.56' '5388.0' '5414.0'
 '5414.0' '5383.0' '5383.0' '5383.0' '5383.0' '5303.5' '5149.0' '5149.0'
 '5046.0' '5012.0' '5012.0' '5012.0' '4906.0' '4757.5' '4747.5' '4778.5'
 '4792.5' '4792.5' '4792.5' '4792.5' '4792.5' '4792.5' '4761.5' '4802.0'
 '4811.0' '4811.0' '4777.0' '4777.0' '4858.0' '4878.0' '4888.0' '4888.0'
 '4888.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0' '4928.0'
 '4928.0' '5271.0' '5218.5' '5206.5' '5206.5' '5208.5' '5208.5' '5208.5'
 '5079.0' '5009.0' '4887.0' '4912.0' '4912.0' '4912.0' '4904.0' '4904.0'
 '4956.0' '4955.0' '4953.0' '4953.0' '4953.0' '4927.0' '4881.0' '4881.0'
 '4901.0' '4900.0' '4900.0' '4900.0' '4900.0' '4900.0' '4825.0' '4877.0'
 '4915.0' '4915.0' '4915.0' '4915.0' '4915.0' '4841.0' '4807.0' '4803.0'
 '4803.0' '4803.0' '4795.56' '4792.22' '4824.44' '4828.89' '4832.22'
 '4832.22' '4832.22' '4832.22' '4764.0' '4783.0' '4741.0' '4741.0'
 '4741.0' '4741.0' '4741.0' '4754.7' '4759.7' '4759.7' '4759.7' '4759.7'
 '4759.7' '4680.5' '4697.0' '4697.0' '4734.0' '4734.0' '4734.0' '4734.0'
 '4734.0' '4734.0' '4797.0' '4809.0' '4809.0' '4809.0' '4809.0' '4832.0'
 '4832.0' '4833.0' '4831.0' '4767.0' '4767.0' '4767.0' '4767.0' '4790.0'
 '4790.0' '4790.0' '4790.0' '4795.56' '4795.56' '4795.56' '4795.56'
 '4803.89' '4871.67' '4893.89' '4893.89' '4893.89' '4963.89' '4996.11'
 '5084.44' '5084.44' '5084.44' '5084.44' '5084.44' '5081.11' '5081.11'
 '5068.89' '5048.89' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22'
 '4992.22' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22' '4992.22'
 '4954.0' '4956.82' '5023.27' '5023.27' '5023.27' '5118.18' '5115.91'
 '5115.91' '5077.73' '5084.09' '5084.09' '5084.09' '5104.09' '5123.18'
 '5121.82' '5122.73' '5091.82' '5091.82' '5091.82' '5036.36' '5032.73'
 '5032.73' '4967.27' '4967.27' '4967.27' '4967.27' '4993.64' '4998.18'
 '4881.82' '4881.82' '4876.55' '4876.55' '4876.55' '4850.91' '4796.36'
 '4748.18' '4748.18' '4797.45' '4797.45' '4797.45' '4816.91' '4855.64'
 '4846.36' '4848.82' '4877.27' '4877.27' '4877.27' '4877.27' '4889.8'
 '4911.4' '4912.9' '4906.6' '4906.6' '4906.6' '4861.6' '4910.0' '4919.0'
 '4868.0' '4868.0' '4868.0' '4868.0' '4722.5' '4672.5' '4518.5' '4489.0'
 '4440.5' '4338.12' '4338.12' '4368.75' '4328.75' '4253.75' '4318.75'
 '4320.62' '4320.62' '4320.62' '4330.0' '4316.25' '4366.88' '4360.0'
 '4493.75' '4493.75' '4493.75' '4567.5' '4565.0' '4584.38' '4584.38'
 '4584.38' '4584.38' '4584.38' '4582.5' '4529.17' '4555.84' '4688.34'
 '4750.84' '4750.84' '4750.84' '4888.33' '4913.34' '4913.34' '4991.66'
 '5007.5' '5007.5' '5007.5' '4993.75' '5001.25' '4986.25' '4913.34'
 '4915.0' '4915.0' '4915.0' '4917.5' '4938.75' '4893.75' '4972.5'
 '4965.62' '4965.62' '4965.62' '4965.62' '4948.75' '4896.5' '4880.66'
 '4903.25' '4903.25' '4903.25' '4884.5' '4880.5' '4871.75' '4880.75'
 '4932.75' '4932.75' '4932.75' '5008.25' '5045.0' '5165.5' '5246.5'
 '5271.5' '5271.5' '5271.5' '5282.0' '5114.0' '5114.0' '5083.25' '5084.75'
 '5084.75' '5084.75' '5104.75' '5081.66' '5031.25' '5031.25' '5031.25'
 '5031.25' '5031.25' '4977.5' '4930.84' '4852.5' '4852.5' '4852.5'
 '4852.5' '4852.5' '4789.84' '4735.66' '4735.66' '4762.66' '4776.66'
 '4776.66' '4776.66' '4825.0' '4817.0' '4831.34' '4831.34' '4874.84'
 '4874.84' '4874.84' '4862.5' '4863.34' '4889.16' '4859.59' '4821.25'
 '4821.25' '4821.25' '4808.75' '4765.0' '4771.25' '4771.25' '4742.5'
 '4742.5' '4742.5' '4750.0' '4731.88' '4731.88' '4717.5' '4726.88'
 '4726.88' '4726.88' '4720.25' '4726.0' '4727.0' '4771.0' '4771.0'
 '4771.0' '4771.0' '4855.0' '4922.5' '4913.75' '4910.0' '4891.25'
 '4891.25' '4891.25' '4755.5' '4759.5' '4749.5' '4740.5' '4739.5' '4739.5'
 '4739.5' '4720.0' '4643.5' '4649.16' '4649.16' '4636.66' '4636.66'
 '4636.66' '4631.66' '4633.75' '4649.0' '4644.0' '4631.25' '4631.25'
 '4631.25' '4626.25' '4618.75' '4572.91' '4635.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:98: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['19040.0' '19040.0' '19040.0' '18900.0' '18900.0' '18840.0' '18840.0'
 '18840.0' '18780.0' '18690.0' '18680.0' '18680.0' '18500.0' '18500.0'
 '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0'
 '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0'
 '18500.0' '18350.0' '18350.0' '18280.0' '18280.0' '17970.0' '17970.0'
 '17970.0' '17970.0' '17930.0' '17930.0' '17930.0' '17930.0' '17930.0'
 '17930.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0'
 '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0'
 '17960.0' '17960.0' '17960.0' '17890.0' '17890.0' '17860.0' '17860.0'
 '17860.0' '17830.0' '17830.0' '17740.0' '17740.0' '17720.0' '17720.0'
 '17720.0' '17660.0' '17610.0' '17490.0' '17400.0' '17300.0' '17300.0'
 '17300.0' '17230.0' '17150.0' '16940.0' '16890.0' '16890.0' '16890.0'
 '16890.0' '16890.0' '16890.0' '16890.0' '16890.0' '16890.0' '16890.0'
 '16890.0' '16890.0' '16680.0' '16680.0' '16510.0' '16420.0' '16420.0'
 '16420.0' '16400.0' '16290.0' '16250.0' '16250.0' '16250.0' '16250.0'
 '16250.0' '16250.0' '16250.0' '16250.0' '16250.0' '16250.0' '16250.0'
 '16160.0' '16140.0' '16140.0' '16140.0' '16140.0' '16140.0' '16140.0'
 '16140.0' '16140.0' '16140.0' '16140.0' '16140.0' '16110.0' '16110.0'
 '16110.0' '15980.0' '15820.0' '15790.0' '15680.0' '15680.0' '15680.0'
 '15680.0' '15460.0' '15340.0' '15210.0' '15040.0' '14870.0' '14870.0'
 '14870.0' '14670.0' '14480.0' '14360.0' '14270.0' '14210.0' '14210.0'
 '14210.0' '14170.0' '14170.0' '14170.0' '14170.0' '14170.0' '14170.0'
 '14170.0' '14170.0' '14170.0' '14170.0' '14170.0' '14090.0' '14090.0'
 '14090.0' '14030.0' '13890.0' '13670.0' '13610.0' '13610.0' '13610.0'
 '13610.0' '13610.0' '13610.0' '13730.0' '13730.0' '13730.0' '13730.0'
 '13730.0' '13710.0' '13710.0' '13710.0' '13710.0' '13710.0' '13710.0'
 '13710.0' '13710.0' '13710.0' '13740.0' '13740.0' '13740.0' '13740.0'
 '13740.0' '13740.0' '13740.0' '13740.0' '13750.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13660.0' '13690.0' '13880.0' '13880.0' '13880.0'
 '13880.0' '14000.0' '14000.0' '14000.0' '14050.0' '14050.0' '14050.0'
 '14050.0' '14130.0' '14160.0' '14180.0' '14340.0' '14340.0' '14410.0'
 '14410.0' '14460.0' '14460.0' '14460.0' '14460.0' '14460.0' '14460.0'
 '14460.0' '14510.0' '14670.0' '14760.0' '14890.0' '14890.0' '14890.0'
 '14890.0' '14900.0' '15020.0' '15270.0' '15380.0' '15480.0' '15480.0'
 '15480.0' '15580.0' '15650.0' '15650.0' '15720.0' '15720.0' '15720.0'
 '15720.0' '15820.0' '15820.0' '15840.0' '15840.0' '15840.0' '15840.0'
 '15840.0' '15840.0' '15840.0' '15840.0' '15840.0' '15840.0' '15840.0'
 '15840.0' '15840.0' '15840.0' '15830.0' '15820.0' '15800.0' '15800.0'
 '15800.0' '15770.0' '15750.0' '15680.0' '15650.0' '15650.0' '15650.0'
 '15650.0' '15650.0' '15650.0' '15650.0' '15550.0' '15500.0' '15500.0'
 '15500.0' '15500.0' '15500.0' '15480.0' '15480.0' '15420.0' '15420.0'
 '15420.0' '15420.0' '15300.0' '15220.0' '15190.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15210.0' '15210.0' '15210.0' '15210.0' '15210.0' '15210.0'
 '15210.0' '15220.0' '15220.0' '15260.0' '15260.0' '15260.0' '15260.0'
 '15260.0' '15280.0' '15330.0' '15330.0' '15480.0' '15480.0' '15560.0'
 '15560.0' '15620.0' '15620.0' '15630.0' '15620.0' '15620.0' '15620.0'
 '15620.0' '15620.0' '15620.0' '15620.0' '15620.0' '15620.0' '15620.0'
 '15620.0' '15610.0' '15610.0' '15560.0' '15560.0' '15560.0' '15560.0'
 '15560.0' '15500.0' '15470.0' '15460.0' '15400.0' '15390.0' '15390.0'
 '15390.0' '15380.0' '15340.0' '15340.0' '15340.0' '15310.0' '15310.0'
 '15310.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15280.0' '15240.0' '15180.0' '15140.0' '15140.0'
 '15140.0' '15130.0' '15130.0' '15070.0' '15000.0' '15000.0' '15000.0'
 '15000.0' '15000.0' '15000.0' '15000.0' '15000.0' '14940.0' '14940.0'
 '14940.0' '14940.0' '14940.0' '14900.0' '14830.0' '14730.0' '14730.0'
 '14730.0' '14690.0' '14590.0' '14550.0' '14540.0' '14470.0' '14470.0'
 '14470.0' '14360.0' '14270.0' '14180.0' '14090.0' '14070.0' '14070.0'
 '14070.0' '13990.0' '13960.0' '13860.0' '13860.0' '13860.0' '13860.0'
 '13860.0' '13740.0' '13690.0' '13680.0' '13680.0' '13680.0' '13680.0'
 '13680.0' '13680.0' '13680.0' '13680.0' '13680.0' '13680.0' '13680.0'
 '13680.0' '13680.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13610.0' '13570.0' '13570.0'
 '13570.0' '13570.0' '13520.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13470.0' '13470.0' '13470.0' '13470.0' '13470.0'
 '13470.0' '13450.0' '13380.0' '13360.0' '13360.0' '13360.0' '13360.0'
 '13360.0' '13340.0' '13250.0' '13230.0' '13230.0' '13230.0' '13230.0'
 '13230.0' '13210.0' '13110.0' '13070.0' '13070.0' '13030.0' '13030.0'
 '13030.0' '12960.0' '12930.0' '12930.0' '12880.0' '12880.0' '12880.0'
 '12880.0' '12820.0' '12760.0' '12680.0' '12590.0' '12560.0' '12560.0'
 '12560.0' '12520.0' '12410.0' '12410.0' '12220.0' '12130.0' '12130.0'
 '12130.0' '12100.0' '12100.0' '12100.0' '12100.0' '12100.0' '12100.0'
 '12100.0' '12100.0' '12100.0' '12100.0' '12100.0' '12090.0' '12090.0'
 '12090.0' '12020.0' '11980.0' '11970.0' '11940.0' '11940.0' '11940.0'
 '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11940.0'
 '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11970.0' '11970.0' '11960.0' '11960.0' '11960.0'
 '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '11960.0'
 '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '12020.0' '12020.0'
 '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0'
 '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0'
 '12020.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12010.0' '12010.0' '11990.0' '11990.0' '11880.0' '11880.0'
 '11880.0' '11860.0' '11810.0' '11810.0' '11810.0' '11750.0' '11750.0'
 '11750.0' '11750.0' '11750.0' '11750.0' '11750.0' '11750.0' '11750.0'
 '11750.0' '11750.0' '11690.0' '11690.0' '11630.0' '11560.0' '11560.0'
 '11560.0' '11560.0' '11560.0' '11520.0' '11520.0' '11510.0' '11510.0'
 '11510.0' '11500.0' '11340.0' '11340.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11210.0' '11180.0' '11180.0' '11180.0'
 '11180.0' '11180.0' '11180.0' '11140.0' '11110.0' '11110.0' '11110.0'
 '11110.0' '11110.0' '11110.0' '11030.0' '10980.0' '10950.0' '10950.0'
 '10950.0' '10890.0' '10870.0' '10870.0' '10870.0' '10870.0' '10870.0'
 '10870.0' '10870.0' '10860.0' '10850.0' '10780.0' '10760.0' '10760.0'
 '10760.0' '10760.0' '10760.0' '10760.0' '10760.0' '10760.0' '10760.0'
 '10760.0' '10690.0' '10690.0' '10670.0' '10670.0' '10650.0' '10650.0'
 '10650.0' '10630.0' '10540.0' '10490.0' '10480.0' '10390.0' '10390.0'
 '10390.0' '10290.0' '10250.0' '10220.0' '10120.0' '10040.0' '10040.0'
 '10040.0' '10040.0' '9960.0' '9880.0' '9880.0' '9880.0' '9880.0' '9880.0'
 '9880.0' '9780.0' '9690.0' '9640.0' '9640.0' '9640.0' '9640.0' '9610.0'
 '9610.0' '9610.0' '9520.0' '9480.0' '9480.0' '9480.0' '9390.0' '9290.0'
 '9210.0' '9210.0' '9180.0' '9180.0' '9180.0' '9180.0' '9000.0' '8990.0'
 '8990.0' '8780.0' '8780.0' '8780.0' '8780.0' '8700.0' '8640.0' '8640.0'
 '8640.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0'
 '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0'
 '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8640.0' '8640.0'
 '8730.0' '8730.0' '8850.0' '8890.0' '8890.0' '8910.0' '8910.0' '8910.0'
 '8910.0' '8910.0' '8920.0' '8940.0' '9050.0' '9050.0' '9090.0' '9330.0'
 '9380.0' '9380.0' '9500.0' '9500.0' '9500.0' '9620.0' '9840.0' '10080.0'
 '10180.0' '10180.0' '10180.0' '10180.0' '10130.0' '9970.0' '10110.0'
 '10000.0' '9980.0' '9980.0' '9980.0' '9780.0' '9590.0' '9590.0' '9590.0'
 '9590.0' '9590.0' '9590.0' '9630.0' '9630.0' '9630.0' '9630.0' '9610.0'
 '9610.0' '9610.0' '9610.0' '9610.0' '9530.0' '9530.0' '9530.0' '9530.0'
 '9530.0' '9570.0' '9570.0' '9570.0' '9540.0' '9360.0' '9360.0' '9360.0'
 '9360.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0'
 '9390.0' '9390.0' '9390.0' '9450.0' '9450.0' '9450.0' '9450.0' '9560.0'
 '9580.0' '9600.0' '9600.0' '9600.0' '9600.0' '9700.0' '9700.0' '9700.0'
 '9700.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_raw_new.loc[:, 'value'] = df_raw_new['value'].astype(str)
/root/project/future_1d/future_alternative.py:110: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['19040.0' '19040.0' '19040.0' '18900.0' '18900.0' '18840.0' '18840.0'
 '18840.0' '18780.0' '18690.0' '18680.0' '18680.0' '18500.0' '18500.0'
 '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0'
 '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0' '18500.0'
 '18500.0' '18350.0' '18350.0' '18280.0' '18280.0' '17970.0' '17970.0'
 '17970.0' '17970.0' '17930.0' '17930.0' '17930.0' '17930.0' '17930.0'
 '17930.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0'
 '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0' '17960.0'
 '17960.0' '17960.0' '17960.0' '17890.0' '17890.0' '17860.0' '17860.0'
 '17860.0' '17830.0' '17830.0' '17740.0' '17740.0' '17720.0' '17720.0'
 '17720.0' '17660.0' '17610.0' '17490.0' '17400.0' '17300.0' '17300.0'
 '17300.0' '17230.0' '17150.0' '16940.0' '16890.0' '16890.0' '16890.0'
 '16890.0' '16890.0' '16890.0' '16890.0' '16890.0' '16890.0' '16890.0'
 '16890.0' '16890.0' '16680.0' '16680.0' '16510.0' '16420.0' '16420.0'
 '16420.0' '16400.0' '16290.0' '16250.0' '16250.0' '16250.0' '16250.0'
 '16250.0' '16250.0' '16250.0' '16250.0' '16250.0' '16250.0' '16250.0'
 '16160.0' '16140.0' '16140.0' '16140.0' '16140.0' '16140.0' '16140.0'
 '16140.0' '16140.0' '16140.0' '16140.0' '16140.0' '16110.0' '16110.0'
 '16110.0' '15980.0' '15820.0' '15790.0' '15680.0' '15680.0' '15680.0'
 '15680.0' '15460.0' '15340.0' '15210.0' '15040.0' '14870.0' '14870.0'
 '14870.0' '14670.0' '14480.0' '14360.0' '14270.0' '14210.0' '14210.0'
 '14210.0' '14170.0' '14170.0' '14170.0' '14170.0' '14170.0' '14170.0'
 '14170.0' '14170.0' '14170.0' '14170.0' '14170.0' '14090.0' '14090.0'
 '14090.0' '14030.0' '13890.0' '13670.0' '13610.0' '13610.0' '13610.0'
 '13610.0' '13610.0' '13610.0' '13730.0' '13730.0' '13730.0' '13730.0'
 '13730.0' '13710.0' '13710.0' '13710.0' '13710.0' '13710.0' '13710.0'
 '13710.0' '13710.0' '13710.0' '13740.0' '13740.0' '13740.0' '13740.0'
 '13740.0' '13740.0' '13740.0' '13740.0' '13750.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0' '13630.0'
 '13630.0' '13630.0' '13660.0' '13690.0' '13880.0' '13880.0' '13880.0'
 '13880.0' '14000.0' '14000.0' '14000.0' '14050.0' '14050.0' '14050.0'
 '14050.0' '14130.0' '14160.0' '14180.0' '14340.0' '14340.0' '14410.0'
 '14410.0' '14460.0' '14460.0' '14460.0' '14460.0' '14460.0' '14460.0'
 '14460.0' '14510.0' '14670.0' '14760.0' '14890.0' '14890.0' '14890.0'
 '14890.0' '14900.0' '15020.0' '15270.0' '15380.0' '15480.0' '15480.0'
 '15480.0' '15580.0' '15650.0' '15650.0' '15720.0' '15720.0' '15720.0'
 '15720.0' '15820.0' '15820.0' '15840.0' '15840.0' '15840.0' '15840.0'
 '15840.0' '15840.0' '15840.0' '15840.0' '15840.0' '15840.0' '15840.0'
 '15840.0' '15840.0' '15840.0' '15830.0' '15820.0' '15800.0' '15800.0'
 '15800.0' '15770.0' '15750.0' '15680.0' '15650.0' '15650.0' '15650.0'
 '15650.0' '15650.0' '15650.0' '15650.0' '15550.0' '15500.0' '15500.0'
 '15500.0' '15500.0' '15500.0' '15480.0' '15480.0' '15420.0' '15420.0'
 '15420.0' '15420.0' '15300.0' '15220.0' '15190.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0' '15180.0'
 '15180.0' '15210.0' '15210.0' '15210.0' '15210.0' '15210.0' '15210.0'
 '15210.0' '15220.0' '15220.0' '15260.0' '15260.0' '15260.0' '15260.0'
 '15260.0' '15280.0' '15330.0' '15330.0' '15480.0' '15480.0' '15560.0'
 '15560.0' '15620.0' '15620.0' '15630.0' '15620.0' '15620.0' '15620.0'
 '15620.0' '15620.0' '15620.0' '15620.0' '15620.0' '15620.0' '15620.0'
 '15620.0' '15610.0' '15610.0' '15560.0' '15560.0' '15560.0' '15560.0'
 '15560.0' '15500.0' '15470.0' '15460.0' '15400.0' '15390.0' '15390.0'
 '15390.0' '15380.0' '15340.0' '15340.0' '15340.0' '15310.0' '15310.0'
 '15310.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0' '15290.0'
 '15290.0' '15290.0' '15280.0' '15240.0' '15180.0' '15140.0' '15140.0'
 '15140.0' '15130.0' '15130.0' '15070.0' '15000.0' '15000.0' '15000.0'
 '15000.0' '15000.0' '15000.0' '15000.0' '15000.0' '14940.0' '14940.0'
 '14940.0' '14940.0' '14940.0' '14900.0' '14830.0' '14730.0' '14730.0'
 '14730.0' '14690.0' '14590.0' '14550.0' '14540.0' '14470.0' '14470.0'
 '14470.0' '14360.0' '14270.0' '14180.0' '14090.0' '14070.0' '14070.0'
 '14070.0' '13990.0' '13960.0' '13860.0' '13860.0' '13860.0' '13860.0'
 '13860.0' '13740.0' '13690.0' '13680.0' '13680.0' '13680.0' '13680.0'
 '13680.0' '13680.0' '13680.0' '13680.0' '13680.0' '13680.0' '13680.0'
 '13680.0' '13680.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0' '13660.0'
 '13660.0' '13660.0' '13660.0' '13660.0' '13610.0' '13570.0' '13570.0'
 '13570.0' '13570.0' '13520.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0' '13480.0'
 '13480.0' '13480.0' '13470.0' '13470.0' '13470.0' '13470.0' '13470.0'
 '13470.0' '13450.0' '13380.0' '13360.0' '13360.0' '13360.0' '13360.0'
 '13360.0' '13340.0' '13250.0' '13230.0' '13230.0' '13230.0' '13230.0'
 '13230.0' '13210.0' '13110.0' '13070.0' '13070.0' '13030.0' '13030.0'
 '13030.0' '12960.0' '12930.0' '12930.0' '12880.0' '12880.0' '12880.0'
 '12880.0' '12820.0' '12760.0' '12680.0' '12590.0' '12560.0' '12560.0'
 '12560.0' '12520.0' '12410.0' '12410.0' '12220.0' '12130.0' '12130.0'
 '12130.0' '12100.0' '12100.0' '12100.0' '12100.0' '12100.0' '12100.0'
 '12100.0' '12100.0' '12100.0' '12100.0' '12100.0' '12090.0' '12090.0'
 '12090.0' '12020.0' '11980.0' '11970.0' '11940.0' '11940.0' '11940.0'
 '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11940.0'
 '11940.0' '11940.0' '11940.0' '11940.0' '11940.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0' '11980.0'
 '11980.0' '11980.0' '11970.0' '11970.0' '11960.0' '11960.0' '11960.0'
 '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '11960.0'
 '11960.0' '11960.0' '11960.0' '11960.0' '11960.0' '12020.0' '12020.0'
 '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0'
 '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0' '12020.0'
 '12020.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0' '12120.0'
 '12120.0' '12120.0' '12120.0' '12120.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0' '12070.0'
 '12070.0' '12010.0' '12010.0' '11990.0' '11990.0' '11880.0' '11880.0'
 '11880.0' '11860.0' '11810.0' '11810.0' '11810.0' '11750.0' '11750.0'
 '11750.0' '11750.0' '11750.0' '11750.0' '11750.0' '11750.0' '11750.0'
 '11750.0' '11750.0' '11690.0' '11690.0' '11630.0' '11560.0' '11560.0'
 '11560.0' '11560.0' '11560.0' '11520.0' '11520.0' '11510.0' '11510.0'
 '11510.0' '11500.0' '11340.0' '11340.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0' '11220.0'
 '11220.0' '11220.0' '11220.0' '11210.0' '11180.0' '11180.0' '11180.0'
 '11180.0' '11180.0' '11180.0' '11140.0' '11110.0' '11110.0' '11110.0'
 '11110.0' '11110.0' '11110.0' '11030.0' '10980.0' '10950.0' '10950.0'
 '10950.0' '10890.0' '10870.0' '10870.0' '10870.0' '10870.0' '10870.0'
 '10870.0' '10870.0' '10860.0' '10850.0' '10780.0' '10760.0' '10760.0'
 '10760.0' '10760.0' '10760.0' '10760.0' '10760.0' '10760.0' '10760.0'
 '10760.0' '10690.0' '10690.0' '10670.0' '10670.0' '10650.0' '10650.0'
 '10650.0' '10630.0' '10540.0' '10490.0' '10480.0' '10390.0' '10390.0'
 '10390.0' '10290.0' '10250.0' '10220.0' '10120.0' '10040.0' '10040.0'
 '10040.0' '10040.0' '9960.0' '9880.0' '9880.0' '9880.0' '9880.0' '9880.0'
 '9880.0' '9780.0' '9690.0' '9640.0' '9640.0' '9640.0' '9640.0' '9610.0'
 '9610.0' '9610.0' '9520.0' '9480.0' '9480.0' '9480.0' '9390.0' '9290.0'
 '9210.0' '9210.0' '9180.0' '9180.0' '9180.0' '9180.0' '9000.0' '8990.0'
 '8990.0' '8780.0' '8780.0' '8780.0' '8780.0' '8700.0' '8640.0' '8640.0'
 '8640.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0'
 '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0'
 '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8620.0' '8640.0' '8640.0'
 '8730.0' '8730.0' '8850.0' '8890.0' '8890.0' '8910.0' '8910.0' '8910.0'
 '8910.0' '8910.0' '8920.0' '8940.0' '9050.0' '9050.0' '9090.0' '9330.0'
 '9380.0' '9380.0' '9500.0' '9500.0' '9500.0' '9620.0' '9840.0' '10080.0'
 '10180.0' '10180.0' '10180.0' '10180.0' '10130.0' '9970.0' '10110.0'
 '10000.0' '9980.0' '9980.0' '9980.0' '9780.0' '9590.0' '9590.0' '9590.0'
 '9590.0' '9590.0' '9590.0' '9630.0' '9630.0' '9630.0' '9630.0' '9610.0'
 '9610.0' '9610.0' '9610.0' '9610.0' '9530.0' '9530.0' '9530.0' '9530.0'
 '9530.0' '9570.0' '9570.0' '9570.0' '9540.0' '9360.0' '9360.0' '9360.0'
 '9360.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0' '9390.0'
 '9390.0' '9390.0' '9390.0' '9450.0' '9450.0' '9450.0' '9450.0' '9560.0'
 '9580.0' '9600.0' '9600.0' '9600.0' '9600.0' '9700.0' '9700.0' '9700.0'
 '9700.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  df_schedule_new.loc[:,'value'] = df_schedule_new['value'].astype(str)
SCI58751_75 lag too short
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