========================================================================= 2025-05-02 17:59:04 Fri START TASK.01 future_1d future_alternative ========================================================================= /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 '['408.0' '395.0' '411.0' ... '667.9' '663.25' '669.0']' 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 '['408.0' '395.0' '411.0' ... '667.9' '663.25' '669.0']' 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 '['1155.0' '1155.0' '1100.0' ... '880.0' '880.0' '880.0']' 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 '['1155.0' '1155.0' '1100.0' ... '880.0' '880.0' '880.0']' 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 '['830.0' '830.0' '825.0' ... '750.0' '755.0' '755.0']' 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 '['830.0' '830.0' '825.0' ... '750.0' '755.0' '755.0']' 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 '['5098.0' '5100.0' '5158.0' ... '4090.0' '3980.0' '3990.0']' 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 '['5098.0' '5100.0' '5158.0' ... '4090.0' '3980.0' '3990.0']' 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 '['4575.0' '4545.0' '4590.0' ... '5995.0' '5870.0' '5850.0']' 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 '['4575.0' '4545.0' '4590.0' ... '5995.0' '5870.0' '5850.0']' 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 '['6780.0' '6790.0' '6850.0' ... '7680.0' '7665.0' '7635.0']' 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 '['6780.0' '6790.0' '6850.0' ... '7680.0' '7665.0' '7635.0']' 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 '['6450.0' '6450.0' '6475.0' ... '7183.0' '7076.0' '7045.0']' 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 '['6450.0' '6450.0' '6475.0' ... '7183.0' '7076.0' '7045.0']' 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 '['6735.0' '6730.0' '6730.0' ... '7500.0' '7385.0' '7435.0']' 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 '['6735.0' '6730.0' '6730.0' ... '7500.0' '7385.0' '7435.0']' 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 '['616.0' '616.0' '629.0' ... '481.0' '470.0' '470.0']' 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 '['616.0' '616.0' '629.0' ... '481.0' '470.0' '470.0']' 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 '['8.5' '7.5' '7.7' '6.6' '6.8' '7.2' '4.8' '4.8' '5.3' '4.3' '2.0' '1.1' '2.2' '2.9' '5.6' '7.7' '5.3' '5.0' '9.0' '10.2' '11.7' '14.4' '15.5' '17.3' '13.9' '15.8' '16.0' '12.7' '13.4' '14.5' '13.6' '15.6' '13.0' '8.3' '8.6' '8.9' '9.1' '7.8' '5.5' '4.9' '4.2' '3.7' '4.2' '6.0' '7.2' '8.5' '6.4' '5.8' '3.1' '2.0' '3.4' '5.6' '4.0' '7.7' '4.1' '3.5' '2.5' '1.5' '1.9' '4.3' '3.1' '6.3' '8.0' '6.8' '5.0' '4.0' '4.5' '7.1' '7.2' '7.7' '1.2' '1.8' '8.5' '10.8' '14.5' '15.3' '12.0' '11.4' '10.2' '7.2' '5.8' '6.6' '8.1' '5.7' '7.2' '8.5' '7.6' '6.7' '5.7' '7.2' '2.4' '3.0' '3.5' '2.9' '2.8' '1.2' '1.4' '0.8' '3.1' '3.4' '6.3' '9.7' '13.1' '15.0' '13.5' '15.8' '17.6' '16.5' '19.0' '16.7' '17.9' '18.3' '12.0' '11.0' '13.6' '13.3' '13.1' '6.7' '6.5' '3.8' '5.5' '11.6' '14.8' '15.4' '9.8' '10.3' '8.9' '9.1' '11.1' '8.6' '11.8' '13.8' '13.5' '13.3' '16.2' '16.5' '7.3' '9.1' '6.5' '3.4' '2.7' '3.5' '6.5' '8.5' '10.7' '10.0' '7.6' '4.8' '6.4' '6.3' '4.2' '5.4' '5.6' '6.9' '5.2' '8.4' '9.5' '9.1' '8.6' '9.5' '8.8' '9.0' '9.5' '8.5' '7.0' '7.0' '7.6' '8.4' '6.5' '7.0' '8.7' '18.2' '22.4' '26.2' '29.4' '28.5' '29.1' '30.3' '31.0' '18.4' '14.4' '16.7' '17.8' '16.9' '16.0' '15.7' '13.0' '13.0' '12.7' '13.5' '15.7' '15.5' '17.1' '18.9' '20.7' '17.5' '16.0' '15.3' '17.6' '18.9' '16.7' '16.8' '19.0' '19.4' '18.3' '14.6' '12.5' '9.0' '9.8' '11.1' '13.1' '11.5' '13.5' '11.0' '9.0' '8.0' '7.7' '10.3' '9.0' '6.6' '7.0' '9.2' '5.9' '7.3' '4.7' '7.9' '4.9' '7.5' '9.4' '14.5' '17.0' '16.0' '18.0' '14.8' '13.9' '12.4' '16.0' '16.7' '17.8' '14.1' '16.9' '14.4' '14.9' '12.0' '8.2' '8.5' '10.0' '9.3' '8.4' '10.2' '12.2' '14.9' '17.0' '14.2' '13.4' '13.9' '16.1' '11.9' '12.7' '12.7' '14.7' '15.7' '19.6' '21.0' '21.6' '19.2' '21.2' '19.8' '19.6' '18.8' '17.8' '14.0' '15.0' '13.3' '15.1' '24.7' '27.6' '25.9' '22.0' '22.9' '26.2' '27.8' '26.8' '31.1' '31.2' '32.3' '25.8' '26.5' '27.8' '29.7' '29.0' '27.4' '26.7' '30.4' '33.1' '30.3' '33.4' '33.3' '28.5' '25.8' '27.5' '29.1' '31.5' '27.6' '25.5' '28.4' '29.9' '31.7' '28.3' '29.0' '28.8' '30.0' '30.1' '31.2' '26.8' '23.0' '25.1' '26.6' '25.6' '23.0' '18.6' '19.1' '13.6' '11.9' '8.7' '10.1' '13.7' '13.6' '14.6' '16.5' '18.4' '17.8' '15.8' '16.6' '16.7' '16.0' '16.8' '17.8' '18.4' '19.6' '17.3' '18.7' '18.5' '16.0' '12.0' '11.3' '13.3' '11.7' '15.4' '13.1' '13.0' '13.5' '13.5' '14.0' '15.2' '15.2' '15.4' '14.0' '11.6' '13.1' '10.8' '13.4' '11.7' '18.3' '17.7' '19.2' '19.8' '16.1' '18.1' '15.0' '16.2' '14.2' '16.5' '14.0' '15.3' '12.7' '15.2' '15.5' '16.0' '7.5' '11.1' '14.2' '22.4' '23.4' '24.5' '26.3' '26.5' '28.0' '24.9' '23.6' '25.6' '26.6' '28.5' '23.9' '27.3' '28.3' '18.2' '20.8' '24.3' '26.9' '27.8' '24.6' '24.4' '26.7' '28.9' '21.5' '24.9' '28.1' '19.2' '23.0']' 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 '['8.5' '7.5' '7.7' '6.6' '6.8' '7.2' '4.8' '4.8' '5.3' '4.3' '2.0' '1.1' '2.2' '2.9' '5.6' '7.7' '5.3' '5.0' '9.0' '10.2' '11.7' '14.4' '15.5' '17.3' '13.9' '15.8' '16.0' '12.7' '13.4' '14.5' '13.6' '15.6' '13.0' '8.3' '8.6' '8.9' '9.1' '7.8' '5.5' '4.9' '4.2' '3.7' '4.2' '6.0' '7.2' '8.5' '6.4' '5.8' '3.1' '2.0' '3.4' '5.6' '4.0' '7.7' '4.1' '3.5' '2.5' '1.5' '1.9' '4.3' '3.1' '6.3' '8.0' '6.8' '5.0' '4.0' '4.5' '7.1' '7.2' '7.7' '1.2' '1.8' '8.5' '10.8' '14.5' '15.3' '12.0' '11.4' '10.2' '7.2' '5.8' '6.6' '8.1' '5.7' '7.2' '8.5' '7.6' '6.7' '5.7' '7.2' '2.4' '3.0' '3.5' '2.9' '2.8' '1.2' '1.4' '0.8' '3.1' '3.4' '6.3' '9.7' '13.1' '15.0' '13.5' '15.8' '17.6' '16.5' '19.0' '16.7' '17.9' '18.3' '12.0' '11.0' '13.6' '13.3' '13.1' '6.7' '6.5' '3.8' '5.5' '11.6' '14.8' '15.4' '9.8' '10.3' '8.9' '9.1' '11.1' '8.6' '11.8' '13.8' '13.5' '13.3' '16.2' '16.5' '7.3' '9.1' '6.5' '3.4' '2.7' '3.5' '6.5' '8.5' '10.7' '10.0' '7.6' '4.8' '6.4' '6.3' '4.2' '5.4' '5.6' '6.9' '5.2' '8.4' '9.5' '9.1' '8.6' '9.5' '8.8' '9.0' '9.5' '8.5' '7.0' '7.0' '7.6' '8.4' '6.5' '7.0' '8.7' '18.2' '22.4' '26.2' '29.4' '28.5' '29.1' '30.3' '31.0' '18.4' '14.4' '16.7' '17.8' '16.9' '16.0' '15.7' '13.0' '13.0' '12.7' '13.5' '15.7' '15.5' '17.1' '18.9' '20.7' '17.5' '16.0' '15.3' '17.6' '18.9' '16.7' '16.8' '19.0' '19.4' '18.3' '14.6' '12.5' '9.0' '9.8' '11.1' '13.1' '11.5' '13.5' '11.0' '9.0' '8.0' '7.7' '10.3' '9.0' '6.6' '7.0' '9.2' '5.9' '7.3' '4.7' '7.9' '4.9' '7.5' '9.4' '14.5' '17.0' '16.0' '18.0' '14.8' '13.9' '12.4' '16.0' '16.7' '17.8' '14.1' '16.9' '14.4' '14.9' '12.0' '8.2' '8.5' '10.0' '9.3' '8.4' '10.2' '12.2' '14.9' '17.0' '14.2' '13.4' '13.9' '16.1' '11.9' '12.7' '12.7' '14.7' '15.7' '19.6' '21.0' '21.6' '19.2' '21.2' '19.8' '19.6' '18.8' '17.8' '14.0' '15.0' '13.3' '15.1' '24.7' '27.6' '25.9' '22.0' '22.9' '26.2' '27.8' '26.8' '31.1' '31.2' '32.3' '25.8' '26.5' '27.8' '29.7' '29.0' '27.4' '26.7' '30.4' '33.1' '30.3' '33.4' '33.3' '28.5' '25.8' '27.5' '29.1' '31.5' '27.6' '25.5' '28.4' '29.9' '31.7' '28.3' '29.0' '28.8' '30.0' '30.1' '31.2' '26.8' '23.0' '25.1' '26.6' '25.6' '23.0' '18.6' '19.1' '13.6' '11.9' '8.7' '10.1' '13.7' '13.6' '14.6' '16.5' '18.4' '17.8' '15.8' '16.6' '16.7' '16.0' '16.8' '17.8' '18.4' '19.6' '17.3' '18.7' '18.5' '16.0' '12.0' '11.3' '13.3' '11.7' '15.4' '13.1' '13.0' '13.5' '13.5' '14.0' '15.2' '15.2' '15.4' '14.0' '11.6' '13.1' '10.8' '13.4' '11.7' '18.3' '17.7' '19.2' '19.8' '16.1' '18.1' '15.0' '16.2' '14.2' '16.5' '14.0' '15.3' '12.7' '15.2' '15.5' '16.0' '7.5' '11.1' '14.2' '22.4' '23.4' '24.5' '26.3' '26.5' '28.0' '24.9' '23.6' '25.6' '26.6' '28.5' '23.9' '27.3' '28.3' '18.2' '20.8' '24.3' '26.9' '27.8' '24.6' '24.4' '26.7' '28.9' '21.5' '24.9' '28.1' '19.2' '23.0']' 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 '['57.37' '68.06' '66.9' '63.55' '64.97' '64.58' '62.65' '62.27' '64.46' '60.05' '64.66' '66.91' '64.97' '68.67' '69.52' '70.49' '73.04' '78.91' '79.11' '77.29' '73.41' '73.28' '72.07' '73.53' '74.13' '75.59' '75.71' '73.16' '69.16' '60.66' '62.48' '60.41' '59.56' '58.96' '57.74' '60.78' '65.39' '70.73' '70.86' '72.31' '75.11' '77.41' '78.26' '84.58' '82.03' '80.94' '78.02' '77.17' '79.72' '79.6' '81.85' '76.32' '78.45' '77.9' '72.56' '77.96' '76.02' '82.15' '75.58' '74.39' '71.75' '74.51' '76.48' '73.96' '74.18' '69.38' '72.93' '76.55' '78.46' '80.69' '84.16' '83.94' '84.0' '80.08' '80.19' '80.75' '70.23' '70.23' '72.24' '60.38' '52.99' '52.15' '55.12' '59.93' '59.15' '58.48' '58.09' '58.53' '58.83' '66.14' '67.82' '72.75' '70.06' '78.74' '83.88' '84.84' '83.02' '82.16' '81.76' '81.23' '77.84' '74.56' '75.24' '68.68' '76.37' '76.55' '76.65' '76.65' '77.31' '75.79' '77.69' '80.44' '82.14' '73.42' '73.42' '73.14' '77.07' '76.88' '84.54' '85.19' '83.89' '79.31' '76.41' '77.44' '82.3' '87.16' '82.2' '77.91' '77.63' '72.02' '69.22' '69.03' '68.29' '68.66' '68.29' '64.08' '68.19' '61.0' '66.7' '64.74' '66.51' '66.23' '65.2' '66.98' '66.42' '66.79' '62.03' '57.82' '60.43' '63.67' '67.84' '66.27' '66.97' '69.13' '62.29' '56.92' '67.19' '72.93' '74.04' '75.61' '74.36' '68.26' '73.07' '76.63' '74.64' '74.59' '75.99' '80.53' '83.68' '79.53' '77.96' '71.17' '72.15' '67.27' '76.16' '79.36' '77.61' '70.33' '64.65' '55.04' '56.06' '61.52' '62.4' '49.29' '53.15' '54.97' '54.24' '59.48' '61.3' '58.68' '58.54' '56.79' '55.62' '58.54' '57.74' '53.51' '55.48' '58.54' '59.7' '61.38' '62.25' '65.67' '66.4' '66.69' '65.63' '63.57' '64.53' '67.22' '68.51' '65.78' '62.59' '59.73' '61.57' '61.57' '61.89' '60.63' '60.81' '63.09' '62.52' '60.5' '65.93' '68.58' '71.42' '71.49' '75.21' '75.15' '74.64' '73.0' '74.2' '73.79' '71.2' '69.48' '69.54' '74.04' '70.4' '67.39' '64.68' '62.89' '66.76' '66.15' '64.32' '61.88' '66.01' '66.58' '66.84' '66.07' '66.01' '65.8' '64.89' '67.17' '61.64' '64.08' '65.04' '63.82' '61.44' '56.77' '60.85' '63.25' '61.58' '61.83' '61.63' '57.42' '62.91' '62.27' '60.84' '54.8' '60.01' '63.74' '62.82' '58.87' '53.7' '58.53' '64.42' '73.67' '70.11' '69.19' '69.19' '69.33' '68.14' '66.73' '66.68' '64.63' '64.95' '63.12' '62.43' '59.33' '58.69' '56.45' '57.46' '60.01' '57.84' '56.96' '57.23' '53.55' '54.84' '55.99' '51.83' '46.38' '46.78' '46.74' '45.36' '47.84' '51.34' '54.58' '46.42' '44.8' '50.91' '53.52' '54.29' '58.48' '59.88' '56.89' '55.43' '56.39' '55.14' '55.01' '57.04' '56.12' '56.0' '59.3' '63.03' '64.18' '63.25' '64.86' '59.58' '60.83' '62.6' '58.61' '58.24' '57.64' '59.38' '59.83' '61.23' '59.68' '60.06' '56.13' '56.2' '58.39' '60.77' '61.19' '53.9' '50.31' '53.11' '55.07' '54.16' '56.78' '57.87' '64.31' '66.39' '64.75' '63.69' '60.53' '64.24' '63.84' '65.11' '64.97' '65.44' '64.35' '61.36' '63.29' '65.08' '62.03' '62.75' '58.18' '60.45' '62.54' '62.11' '61.6' '62.5' '61.83' '61.65' '59.52' '61.33' '61.76' '59.67' '59.7' '63.84' '65.91' '65.98' '69.17' '68.19' '70.74' '69.83' '65.8' '67.41' '63.98' '65.77' '63.24' '60.75' '60.86' '59.42' '59.42' '57.81' '58.16' '58.79' '61.81' '63.56' '63.73' '63.21' '64.26' '65.75' '66.98' '66.47' '64.03' '63.7' '65.89' '65.22' '67.1']' 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 '['57.37' '68.06' '66.9' '63.55' '64.97' '64.58' '62.65' '62.27' '64.46' '60.05' '64.66' '66.91' '64.97' '68.67' '69.52' '70.49' '73.04' '78.91' '79.11' '77.29' '73.41' '73.28' '72.07' '73.53' '74.13' '75.59' '75.71' '73.16' '69.16' '60.66' '62.48' '60.41' '59.56' '58.96' '57.74' '60.78' '65.39' '70.73' '70.86' '72.31' '75.11' '77.41' '78.26' '84.58' '82.03' '80.94' '78.02' '77.17' '79.72' '79.6' '81.85' '76.32' '78.45' '77.9' '72.56' '77.96' '76.02' '82.15' '75.58' '74.39' '71.75' '74.51' '76.48' '73.96' '74.18' '69.38' '72.93' '76.55' '78.46' '80.69' '84.16' '83.94' '84.0' '80.08' '80.19' '80.75' '70.23' '70.23' '72.24' '60.38' '52.99' '52.15' '55.12' '59.93' '59.15' '58.48' '58.09' '58.53' '58.83' '66.14' '67.82' '72.75' '70.06' '78.74' '83.88' '84.84' '83.02' '82.16' '81.76' '81.23' '77.84' '74.56' '75.24' '68.68' '76.37' '76.55' '76.65' '76.65' '77.31' '75.79' '77.69' '80.44' '82.14' '73.42' '73.42' '73.14' '77.07' '76.88' '84.54' '85.19' '83.89' '79.31' '76.41' '77.44' '82.3' '87.16' '82.2' '77.91' '77.63' '72.02' '69.22' '69.03' '68.29' '68.66' '68.29' '64.08' '68.19' '61.0' '66.7' '64.74' '66.51' '66.23' '65.2' '66.98' '66.42' '66.79' '62.03' '57.82' '60.43' '63.67' '67.84' '66.27' '66.97' '69.13' '62.29' '56.92' '67.19' '72.93' '74.04' '75.61' '74.36' '68.26' '73.07' '76.63' '74.64' '74.59' '75.99' '80.53' '83.68' '79.53' '77.96' '71.17' '72.15' '67.27' '76.16' '79.36' '77.61' '70.33' '64.65' '55.04' '56.06' '61.52' '62.4' '49.29' '53.15' '54.97' '54.24' '59.48' '61.3' '58.68' '58.54' '56.79' '55.62' '58.54' '57.74' '53.51' '55.48' '58.54' '59.7' '61.38' '62.25' '65.67' '66.4' '66.69' '65.63' '63.57' '64.53' '67.22' '68.51' '65.78' '62.59' '59.73' '61.57' '61.57' '61.89' '60.63' '60.81' '63.09' '62.52' '60.5' '65.93' '68.58' '71.42' '71.49' '75.21' '75.15' '74.64' '73.0' '74.2' '73.79' '71.2' '69.48' '69.54' '74.04' '70.4' '67.39' '64.68' '62.89' '66.76' '66.15' '64.32' '61.88' '66.01' '66.58' '66.84' '66.07' '66.01' '65.8' '64.89' '67.17' '61.64' '64.08' '65.04' '63.82' '61.44' '56.77' '60.85' '63.25' '61.58' '61.83' '61.63' '57.42' '62.91' '62.27' '60.84' '54.8' '60.01' '63.74' '62.82' '58.87' '53.7' '58.53' '64.42' '73.67' '70.11' '69.19' '69.19' '69.33' '68.14' '66.73' '66.68' '64.63' '64.95' '63.12' '62.43' '59.33' '58.69' '56.45' '57.46' '60.01' '57.84' '56.96' '57.23' '53.55' '54.84' '55.99' '51.83' '46.38' '46.78' '46.74' '45.36' '47.84' '51.34' '54.58' '46.42' '44.8' '50.91' '53.52' '54.29' '58.48' '59.88' '56.89' '55.43' '56.39' '55.14' '55.01' '57.04' '56.12' '56.0' '59.3' '63.03' '64.18' '63.25' '64.86' '59.58' '60.83' '62.6' '58.61' '58.24' '57.64' '59.38' '59.83' '61.23' '59.68' '60.06' '56.13' '56.2' '58.39' '60.77' '61.19' '53.9' '50.31' '53.11' '55.07' '54.16' '56.78' '57.87' '64.31' '66.39' '64.75' '63.69' '60.53' '64.24' '63.84' '65.11' '64.97' '65.44' '64.35' '61.36' '63.29' '65.08' '62.03' '62.75' '58.18' '60.45' '62.54' '62.11' '61.6' '62.5' '61.83' '61.65' '59.52' '61.33' '61.76' '59.67' '59.7' '63.84' '65.91' '65.98' '69.17' '68.19' '70.74' '69.83' '65.8' '67.41' '63.98' '65.77' '63.24' '60.75' '60.86' '59.42' '59.42' '57.81' '58.16' '58.79' '61.81' '63.56' '63.73' '63.21' '64.26' '65.75' '66.98' '66.47' '64.03' '63.7' '65.89' '65.22' '67.1']' 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 '['83.3' '83.3' '83.4' '83.4' '83.4' '84.6' '84.8' '84.8' '85.2' '85.2' '85.2' '85.2' '85.2' '85.2' '87.0' '88.7' '80.3' '75.0' '73.0' '76.6' '83.9' '88.6' '89.9' '88.4' '87.8' '89.5' '87.2' '87.5' '88.9' '89.2' '89.3' '88.3' '88.1' '88.0' '89.6' '91.0' '91.2' '90.4' '92.6' '92.2' '92.2' '92.8' '92.4' '92.4' '92.4' '91.9' '91.1' '91.1' '90.2' '90.2' '91.0' '94.3' '95.2' '97.0' '96.4' '97.1' '95.4' '95.4' '95.4' '95.4' '94.8' '93.9' '93.7' '93.4' '93.4' '93.1' '93.1' '90.7' '86.0' '83.8' '77.2' '75.0' '83.1' '88.9' '91.7' '93.2' '93.2' '93.2' '95.1' '96.8' '97.1' '96.2' '96.2' '96.5' '95.4' '94.3' '94.9' '94.9' '94.4' '92.4' '92.6' '93.0' '93.2' '94.4' '92.9' '93.3' '95.2' '95.1' '95.0' '94.7' '90.1' '83.3' '80.2' '85.0' '88.6' '88.9' '90.1' '88.9' '84.8' '85.7' '85.9' '85.5' '85.2' '85.0' '84.7' '82.3' '81.8' '78.0' '71.7' '67.5' '73.0' '82.7' '84.5' '86.7' '88.9' '89.3' '92.1' '92.0' '90.0' '91.2' '91.2' '91.7' '90.0' '87.9' '86.3' '86.7' '87.1' '89.4' '90.6' '91.1' '91.5' '87.9' '85.6' '85.3' '85.1' '85.5' '86.8' '86.9' '89.5' '88.5' '88.5' '89.5' '90.0' '90.0' '89.2' '88.7' '89.0' '88.4' '88.4' '89.1' '88.9' '88.7' '87.5' '84.5' '83.9' '83.9' '81.3' '75.7' '71.6' '70.0' '54.0' '53.2' '54.5' '57.2' '66.5' '77.4' '78.2' '78.6' '78.0' '77.4' '79.9' '79.2' '78.9' '78.5' '79.3' '79.1' '80.5' '81.2' '84.2' '83.1' '83.8' '84.8' '84.8' '82.6' '82.7' '82.2' '81.4' '81.6' '82.6' '82.6' '82.7' '83.4' '83.5' '83.1' '82.0' '82.2' '83.4' '84.0' '84.0' '83.6' '83.6' '83.6' '83.6' '83.1' '83.1' '83.3' '80.8' '81.9' '85.3' '81.7' '80.4' '74.2' '73.0' '72.3' '74.5' '79.6' '82.0' '85.0' '85.4' '85.6' '86.0' '86.1' '85.8' '85.9' '88.5' '87.4' '86.3' '84.4' '86.3' '85.4' '87.0' '89.1' '89.1' '89.5' '90.0' '89.7' '90.2' '90.7' '91.1' '91.3' '85.5' '87.1' '85.2' '85.4' '84.1' '78.8' '75.7' '77.2' '76.3' '78.0' '77.7' '78.5' '78.5' '83.3' '81.6' '77.1' '76.8' '75.8' '76.5' '76.5' '76.2' '79.9' '78.5' '76.0' '78.1' '82.6' '86.4' '87.0' '87.9' '90.5' '89.3' '87.3' '84.4' '72.2' '71.1' '70.2' '70.2' '71.9' '73.4' '73.0' '72.2' '73.2' '73.0' '74.1' '71.0' '65.6' '62.6' '63.1' '64.9' '67.1' '68.7' '68.8' '68.9' '72.8' '73.1' '75.1' '73.6' '74.3' '73.4' '73.4' '72.8' '73.1' '73.0' '69.3' '64.8' '61.6' '60.0' '55.8' '53.9' '51.9' '49.7' '47.5' '49.3' '47.7' '48.0' '48.4' '48.7' '51.9' '67.6' '74.1' '78.4' '79.1' '81.6' '82.4' '84.6' '85.1' '83.7' '82.2' '81.1' '79.5' '78.2' '82.5' '83.8' '84.7' '87.2' '87.5' '87.5' '87.6' '88.2' '88.6' '89.5' '90.1' '90.7' '90.8' '90.5' '90.6' '90.6' '90.5' '90.9' '91.3' '86.9' '87.4' '88.2' '89.9' '91.4' '91.4' '90.8' '90.9' '90.4' '90.3' '90.2' '89.7' '88.6' '88.5' '88.4' '90.4' '90.8' '89.1' '84.5' '82.3' '80.4' '80.6' '85.6' '90.1' '91.4' '92.4' '91.7' '92.3' '92.5' '93.7' '94.2' '94.2' '94.2' '90.6' '88.9' '89.9' '90.0' '89.9' '89.8' '91.4' '88.0' '88.2' '88.4' '88.2' '89.2' '89.1' '87.7' '87.4' '88.1' '89.3' '89.2' '89.7']' 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 '['83.3' '83.3' '83.4' '83.4' '83.4' '84.6' '84.8' '84.8' '85.2' '85.2' '85.2' '85.2' '85.2' '85.2' '87.0' '88.7' '80.3' '75.0' '73.0' '76.6' '83.9' '88.6' '89.9' '88.4' '87.8' '89.5' '87.2' '87.5' '88.9' '89.2' '89.3' '88.3' '88.1' '88.0' '89.6' '91.0' '91.2' '90.4' '92.6' '92.2' '92.2' '92.8' '92.4' '92.4' '92.4' '91.9' '91.1' '91.1' '90.2' '90.2' '91.0' '94.3' '95.2' '97.0' '96.4' '97.1' '95.4' '95.4' '95.4' '95.4' '94.8' '93.9' '93.7' '93.4' '93.4' '93.1' '93.1' '90.7' '86.0' '83.8' '77.2' '75.0' '83.1' '88.9' '91.7' '93.2' '93.2' '93.2' '95.1' '96.8' '97.1' '96.2' '96.2' '96.5' '95.4' '94.3' '94.9' '94.9' '94.4' '92.4' '92.6' '93.0' '93.2' '94.4' '92.9' '93.3' '95.2' '95.1' '95.0' '94.7' '90.1' '83.3' '80.2' '85.0' '88.6' '88.9' '90.1' '88.9' '84.8' '85.7' '85.9' '85.5' '85.2' '85.0' '84.7' '82.3' '81.8' '78.0' '71.7' '67.5' '73.0' '82.7' '84.5' '86.7' '88.9' '89.3' '92.1' '92.0' '90.0' '91.2' '91.2' '91.7' '90.0' '87.9' '86.3' '86.7' '87.1' '89.4' '90.6' '91.1' '91.5' '87.9' '85.6' '85.3' '85.1' '85.5' '86.8' '86.9' '89.5' '88.5' '88.5' '89.5' '90.0' '90.0' '89.2' '88.7' '89.0' '88.4' '88.4' '89.1' '88.9' '88.7' '87.5' '84.5' '83.9' '83.9' '81.3' '75.7' '71.6' '70.0' '54.0' '53.2' '54.5' '57.2' '66.5' '77.4' '78.2' '78.6' '78.0' '77.4' '79.9' '79.2' '78.9' '78.5' '79.3' '79.1' '80.5' '81.2' '84.2' '83.1' '83.8' '84.8' '84.8' '82.6' '82.7' '82.2' '81.4' '81.6' '82.6' '82.6' '82.7' '83.4' '83.5' '83.1' '82.0' '82.2' '83.4' '84.0' '84.0' '83.6' '83.6' '83.6' '83.6' '83.1' '83.1' '83.3' '80.8' '81.9' '85.3' '81.7' '80.4' '74.2' '73.0' '72.3' '74.5' '79.6' '82.0' '85.0' '85.4' '85.6' '86.0' '86.1' '85.8' '85.9' '88.5' '87.4' '86.3' '84.4' '86.3' '85.4' '87.0' '89.1' '89.1' '89.5' '90.0' '89.7' '90.2' '90.7' '91.1' '91.3' '85.5' '87.1' '85.2' '85.4' '84.1' '78.8' '75.7' '77.2' '76.3' '78.0' '77.7' '78.5' '78.5' '83.3' '81.6' '77.1' '76.8' '75.8' '76.5' '76.5' '76.2' '79.9' '78.5' '76.0' '78.1' '82.6' '86.4' '87.0' '87.9' '90.5' '89.3' '87.3' '84.4' '72.2' '71.1' '70.2' '70.2' '71.9' '73.4' '73.0' '72.2' '73.2' '73.0' '74.1' '71.0' '65.6' '62.6' '63.1' '64.9' '67.1' '68.7' '68.8' '68.9' '72.8' '73.1' '75.1' '73.6' '74.3' '73.4' '73.4' '72.8' '73.1' '73.0' '69.3' '64.8' '61.6' '60.0' '55.8' '53.9' '51.9' '49.7' '47.5' '49.3' '47.7' '48.0' '48.4' '48.7' '51.9' '67.6' '74.1' '78.4' '79.1' '81.6' '82.4' '84.6' '85.1' '83.7' '82.2' '81.1' '79.5' '78.2' '82.5' '83.8' '84.7' '87.2' '87.5' '87.5' '87.6' '88.2' '88.6' '89.5' '90.1' '90.7' '90.8' '90.5' '90.6' '90.6' '90.5' '90.9' '91.3' '86.9' '87.4' '88.2' '89.9' '91.4' '91.4' '90.8' '90.9' '90.4' '90.3' '90.2' '89.7' '88.6' '88.5' '88.4' '90.4' '90.8' '89.1' '84.5' '82.3' '80.4' '80.6' '85.6' '90.1' '91.4' '92.4' '91.7' '92.3' '92.5' '93.7' '94.2' '94.2' '94.2' '90.6' '88.9' '89.9' '90.0' '89.9' '89.8' '91.4' '88.0' '88.2' '88.4' '88.2' '89.2' '89.1' '87.7' '87.4' '88.1' '89.3' '89.2' '89.7']' 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 '['79.8' '79.8' '82.5' '83.1' '83.1' '83.1' '81.3' '81.3' '81.8' '81.8' '83.8' '83.8' '83.8' '84.2' '86.6' '84.6' '80.0' '69.3' '73.7' '80.6' '82.1' '82.1' '83.4' '86.0' '84.4' '84.4' '81.0' '82.8' '81.5' '81.5' '82.5' '80.8' '80.8' '81.7' '83.1' '81.2' '83.5' '84.1' '83.7' '84.1' '84.6' '85.0' '85.0' '84.4' '84.4' '84.4' '83.0' '80.4' '82.5' '82.5' '84.3' '87.4' '87.4' '87.9' '88.6' '87.0' '87.6' '90.4' '90.9' '90.9' '90.9' '90.9' '86.7' '88.2' '89.2' '86.5' '86.5' '84.2' '81.6' '69.4' '71.1' '79.0' '79.8' '85.3' '87.0' '86.8' '89.6' '88.2' '89.9' '91.2' '91.2' '92.3' '92.3' '92.3' '90.1' '91.5' '92.5' '90.7' '87.1' '89.3' '89.3' '92.7' '92.8' '91.1' '84.0' '83.9' '77.9' '71.9' '72.0' '68.0' '57.4' '60.1' '59.8' '60.6' '75.1' '76.9' '79.1' '77.0' '77.0' '83.8' '85.5' '86.5' '87.1' '87.1' '87.4' '83.9' '83.9' '80.3' '63.7' '73.1' '84.7' '84.5' '87.7' '87.7' '87.7' '88.7' '90.6' '89.0' '89.0' '89.0' '84.6' '78.5' '75.0' '73.0' '75.0' '80.0' '80.9' '80.9' '87.8' '88.9' '88.9' '85.0' '85.0' '82.7' '82.7' '83.7' '85.8' '88.3' '88.5' '87.0' '89.4' '91.4' '91.4' '90.0' '87.0' '87.0' '85.5' '86.7' '87.6' '88.8' '84.6' '83.6' '83.6' '82.0' '82.0' '81.9' '75.0' '67.6' '58.6' '50.0' '41.2' '42.5' '49.0' '64.4' '72.7' '76.1' '76.1' '86.2' '84.1' '83.6' '92.5' '94.5' '94.5' '94.8' '94.7' '94.7' '94.7' '94.7' '94.7' '95.8' '95.8' '93.6' '94.3' '94.3' '94.3' '94.7' '92.3' '92.3' '95.1' '95.1' '97.9' '97.9' '99.3' '99.3' '99.3' '99.3' '99.3' '99.3' '99.3' '99.3' '99.3' '100.0' '98.4' '100.4' '97.3' '95.9' '95.9' '93.1' '96.2' '89.6' '95.3' '90.5' '86.8' '86.6' '90.1' '93.8' '96.6' '95.5' '98.5' '99.4' '98.9' '96.6' '96.6' '94.2' '97.4' '97.4' '93.2' '92.9' '94.6' '93.2' '92.4' '93.9' '92.5' '94.1' '95.3' '98.0' '96.1' '88.4' '85.2' '82.5' '82.3' '81.8' '80.0' '79.2' '74.4' '75.5' '71.1' '69.4' '72.5' '77.1' '83.4' '83.7' '84.6' '83.4' '85.9' '81.8' '81.3' '81.5' '82.0' '83.8' '84.6' '80.9' '78.8' '74.0' '67.6' '66.8' '73.0' '85.8' '85.0' '87.2' '82.7' '79.4' '78.3' '71.7' '66.7' '61.5' '63.2' '61.6' '57.7' '65.7' '67.2' '74.3' '78.2' '80.0' '78.5' '77.7' '76.9' '82.7' '83.1' '85.2' '86.4' '86.0' '85.7' '80.8' '81.6' '80.6' '73.9' '75.9' '73.0' '74.4' '76.0' '78.4' '82.4' '82.4' '82.8' '83.5' '83.5' '83.5' '79.4' '76.6' '76.6' '76.6' '75.6' '82.0' '71.9' '61.1' '60.8' '68.9' '68.6' '73.8' '75.0' '84.3' '84.6' '85.2' '88.8' '88.5' '85.0' '84.0' '79.8' '77.5' '73.8' '73.7' '75.1' '77.1' '79.7' '79.0' '82.1' '82.1' '82.5' '82.6' '84.2' '86.9' '86.3' '86.3' '86.5' '87.7' '87.7' '87.4' '87.4' '87.0' '85.0' '84.5' '85.0' '87.7' '88.0' '88.0' '87.8' '88.1' '87.3' '87.6' '88.4' '87.7' '87.7' '87.7' '87.7' '86.4' '83.3' '86.1' '85.2' '81.8' '75.2' '72.4' '60.9' '64.8' '71.6' '78.2' '77.5' '82.6' '87.6' '86.3' '84.9' '84.4' '84.1' '85.9' '85.9' '85.7' '85.6' '87.7' '87.9' '88.3' '86.8' '86.8' '85.2' '85.0' '84.8' '84.8' '84.7' '79.1' '79.1' '77.8' '78.1' '79.8' '80.6' '82.2']' 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 '['79.8' '79.8' '82.5' '83.1' '83.1' '83.1' '81.3' '81.3' '81.8' '81.8' '83.8' '83.8' '83.8' '84.2' '86.6' '84.6' '80.0' '69.3' '73.7' '80.6' '82.1' '82.1' '83.4' '86.0' '84.4' '84.4' '81.0' '82.8' '81.5' '81.5' '82.5' '80.8' '80.8' '81.7' '83.1' '81.2' '83.5' '84.1' '83.7' '84.1' '84.6' '85.0' '85.0' '84.4' '84.4' '84.4' '83.0' '80.4' '82.5' '82.5' '84.3' '87.4' '87.4' '87.9' '88.6' '87.0' '87.6' '90.4' '90.9' '90.9' '90.9' '90.9' '86.7' '88.2' '89.2' '86.5' '86.5' '84.2' '81.6' '69.4' '71.1' '79.0' '79.8' '85.3' '87.0' '86.8' '89.6' '88.2' '89.9' '91.2' '91.2' '92.3' '92.3' '92.3' '90.1' '91.5' '92.5' '90.7' '87.1' '89.3' '89.3' '92.7' '92.8' '91.1' '84.0' '83.9' '77.9' '71.9' '72.0' '68.0' '57.4' '60.1' '59.8' '60.6' '75.1' '76.9' '79.1' '77.0' '77.0' '83.8' '85.5' '86.5' '87.1' '87.1' '87.4' '83.9' '83.9' '80.3' '63.7' '73.1' '84.7' '84.5' '87.7' '87.7' '87.7' '88.7' '90.6' '89.0' '89.0' '89.0' '84.6' '78.5' '75.0' '73.0' '75.0' '80.0' '80.9' '80.9' '87.8' '88.9' '88.9' '85.0' '85.0' '82.7' '82.7' '83.7' '85.8' '88.3' '88.5' '87.0' '89.4' '91.4' '91.4' '90.0' '87.0' '87.0' '85.5' '86.7' '87.6' '88.8' '84.6' '83.6' '83.6' '82.0' '82.0' '81.9' '75.0' '67.6' '58.6' '50.0' '41.2' '42.5' '49.0' '64.4' '72.7' '76.1' '76.1' '86.2' '84.1' '83.6' '92.5' '94.5' '94.5' '94.8' '94.7' '94.7' '94.7' '94.7' '94.7' '95.8' '95.8' '93.6' '94.3' '94.3' '94.3' '94.7' '92.3' '92.3' '95.1' '95.1' '97.9' '97.9' '99.3' '99.3' '99.3' '99.3' '99.3' '99.3' '99.3' '99.3' '99.3' '100.0' '98.4' '100.4' '97.3' '95.9' '95.9' '93.1' '96.2' '89.6' '95.3' '90.5' '86.8' '86.6' '90.1' '93.8' '96.6' '95.5' '98.5' '99.4' '98.9' '96.6' '96.6' '94.2' '97.4' '97.4' '93.2' '92.9' '94.6' '93.2' '92.4' '93.9' '92.5' '94.1' '95.3' '98.0' '96.1' '88.4' '85.2' '82.5' '82.3' '81.8' '80.0' '79.2' '74.4' '75.5' '71.1' '69.4' '72.5' '77.1' '83.4' '83.7' '84.6' '83.4' '85.9' '81.8' '81.3' '81.5' '82.0' '83.8' '84.6' '80.9' '78.8' '74.0' '67.6' '66.8' '73.0' '85.8' '85.0' '87.2' '82.7' '79.4' '78.3' '71.7' '66.7' '61.5' '63.2' '61.6' '57.7' '65.7' '67.2' '74.3' '78.2' '80.0' '78.5' '77.7' '76.9' '82.7' '83.1' '85.2' '86.4' '86.0' '85.7' '80.8' '81.6' '80.6' '73.9' '75.9' '73.0' '74.4' '76.0' '78.4' '82.4' '82.4' '82.8' '83.5' '83.5' '83.5' '79.4' '76.6' '76.6' '76.6' '75.6' '82.0' '71.9' '61.1' '60.8' '68.9' '68.6' '73.8' '75.0' '84.3' '84.6' '85.2' '88.8' '88.5' '85.0' '84.0' '79.8' '77.5' '73.8' '73.7' '75.1' '77.1' '79.7' '79.0' '82.1' '82.1' '82.5' '82.6' '84.2' '86.9' '86.3' '86.3' '86.5' '87.7' '87.7' '87.4' '87.4' '87.0' '85.0' '84.5' '85.0' '87.7' '88.0' '88.0' '87.8' '88.1' '87.3' '87.6' '88.4' '87.7' '87.7' '87.7' '87.7' '86.4' '83.3' '86.1' '85.2' '81.8' '75.2' '72.4' '60.9' '64.8' '71.6' '78.2' '77.5' '82.6' '87.6' '86.3' '84.9' '84.4' '84.1' '85.9' '85.9' '85.7' '85.6' '87.7' '87.9' '88.3' '86.8' '86.8' '85.2' '85.0' '84.8' '84.8' '84.7' '79.1' '79.1' '77.8' '78.1' '79.8' '80.6' '82.2']' 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 '['77.0' '76.9' '76.4' '76.0' '78.6' '78.2' '74.8' '73.5' '69.3' '69.3' '68.6' '70.8' '78.7' '78.0' '77.2' '81.0' '81.0' '78.0' '77.0' '76.0' '76.0' '76.0' '77.3' '77.3' '78.5' '80.5' '83.0' '80.9' '78.9' '83.1' '81.4' '81.4' '83.1' '84.7' '83.5' '84.1' '84.1' '83.4' '84.1' '85.3' '84.5' '82.9' '81.3' '81.7' '84.9' '85.7' '84.3' '84.2' '84.2' '85.3' '89.0' '89.0' '83.3' '81.7' '82.6' '79.1' '78.0' '78.9' '75.5' '77.5' '70.2' '69.8' '72.4' '79.7' '74.7' '76.8' '77.8' '84.4' '87.3' '88.3' '88.3' '86.5' '86.0' '86.0' '86.8' '86.8' '87.0' '90.0' '91.0' '91.0' '92.0' '92.6' '92.6' '90.5' '88.2' '88.4' '85.0' '83.0' '80.0' '83.0' '84.7' '86.9' '90.6' '85.7' '82.3' '78.9' '79.0' '78.3' '73.0' '71.8' '72.0' '70.3' '73.6' '74.6' '77.4' '73.2' '73.7' '80.1' '80.6' '80.7' '83.0' '78.0' '70.9' '69.8' '72.1' '73.7' '75.9' '79.8' '76.8' '76.0' '75.8' '78.7' '78.2' '80.5' '81.7' '82.4' '82.4' '84.0' '85.0' '86.0' '85.8' '84.8' '85.3' '85.7' '86.0' '86.0' '85.7' '82.2' '81.4' '79.7' '75.9' '72.4' '73.5' '77.2' '82.2' '83.3' '83.5' '83.1' '79.3' '73.4' '79.3' '79.3' '78.2' '72.6' '69.0' '71.0' '69.8' '70.0' '75.6' '75.2' '73.3' '64.7' '64.7' '65.6' '75.0' '67.8' '60.4' '59.0' '59.5' '63.6' '70.7' '72.7' '74.6' '75.9' '75.9' '80.0' '76.2' '74.3' '74.3' '74.3' '74.5' '74.5' '75.0' '75.0' '75.9' '77.6' '77.3' '77.6' '77.9' '79.0' '78.8' '79.7' '80.2' '80.5' '83.2' '83.7' '82.6' '83.7' '84.0' '79.8' '76.4' '75.9' '75.9' '75.0' '69.8' '74.5' '77.8' '76.7' '76.1' '75.7' '76.7' '76.2' '75.9' '75.6' '74.3' '72.3' '75.8' '76.6' '78.2' '78.5' '79.0' '80.1' '79.8' '73.1' '76.0' '78.0' '82.0' '81.0' '82.1' '81.8' '81.8' '81.8' '81.8' '81.6' '81.7' '84.0' '82.2' '79.6' '79.1' '77.7' '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_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 '['77.0' '76.9' '76.4' '76.0' '78.6' '78.2' '74.8' '73.5' '69.3' '69.3' '68.6' '70.8' '78.7' '78.0' '77.2' '81.0' '81.0' '78.0' '77.0' '76.0' '76.0' '76.0' '77.3' '77.3' '78.5' '80.5' '83.0' '80.9' '78.9' '83.1' '81.4' '81.4' '83.1' '84.7' '83.5' '84.1' '84.1' '83.4' '84.1' '85.3' '84.5' '82.9' '81.3' '81.7' '84.9' '85.7' '84.3' '84.2' '84.2' '85.3' '89.0' '89.0' '83.3' '81.7' '82.6' '79.1' '78.0' '78.9' '75.5' '77.5' '70.2' '69.8' '72.4' '79.7' '74.7' '76.8' '77.8' '84.4' '87.3' '88.3' '88.3' '86.5' '86.0' '86.0' '86.8' '86.8' '87.0' '90.0' '91.0' '91.0' '92.0' '92.6' '92.6' '90.5' '88.2' '88.4' '85.0' '83.0' '80.0' '83.0' '84.7' '86.9' '90.6' '85.7' '82.3' '78.9' '79.0' '78.3' '73.0' '71.8' '72.0' '70.3' '73.6' '74.6' '77.4' '73.2' '73.7' '80.1' '80.6' '80.7' '83.0' '78.0' '70.9' '69.8' '72.1' '73.7' '75.9' '79.8' '76.8' '76.0' '75.8' '78.7' '78.2' '80.5' '81.7' '82.4' '82.4' '84.0' '85.0' '86.0' '85.8' '84.8' '85.3' '85.7' '86.0' '86.0' '85.7' '82.2' '81.4' '79.7' '75.9' '72.4' '73.5' '77.2' '82.2' '83.3' '83.5' '83.1' '79.3' '73.4' '79.3' '79.3' '78.2' '72.6' '69.0' '71.0' '69.8' '70.0' '75.6' '75.2' '73.3' '64.7' '64.7' '65.6' '75.0' '67.8' '60.4' '59.0' '59.5' '63.6' '70.7' '72.7' '74.6' '75.9' '75.9' '80.0' '76.2' '74.3' '74.3' '74.3' '74.5' '74.5' '75.0' '75.0' '75.9' '77.6' '77.3' '77.6' '77.9' '79.0' '78.8' '79.7' '80.2' '80.5' '83.2' '83.7' '82.6' '83.7' '84.0' '79.8' '76.4' '75.9' '75.9' '75.0' '69.8' '74.5' '77.8' '76.7' '76.1' '75.7' '76.7' '76.2' '75.9' '75.6' '74.3' '72.3' '75.8' '76.6' '78.2' '78.5' '79.0' '80.1' '79.8' '73.1' '76.0' '78.0' '82.0' '81.0' '82.1' '81.8' '81.8' '81.8' '81.8' '81.6' '81.7' '84.0' '82.2' '79.6' '79.1' '77.7' '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']' 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' 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'4493.75' '4493.75' '4493.75' '4567.5' '4565.0' '4584.38' '4584.38' '4584.38']' 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. 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'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']' 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' 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'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']' 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']' 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' 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'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' 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'4493.75' '4493.75' '4493.75' '4567.5' '4565.0' '4584.38' '4584.38' '4584.38']' 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. 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'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']' 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. 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'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']' 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 SCI88054_346 lag too short SCI70421_92 lag too short SCI69184_171 lag too short SCI69337_171 lag too short SCI107831_346 lag too short SCI110416_89 lag too short SCI78466_346 lag too short ID01211519 lag too short SMM_a10015984 lag too short SMM_a10015956 lag too short SMM_a10016363 lag too short SCI58766_80 lag too short SMM_a10031627 lag too short SMM_a10016212 lag too short SCI58662_75 lag too short SCI58446_75 lag too short SCI61123_71 lag too short ID01167342 lag too short SMM_a10124268 lag too short SMM_a10124267 lag too short ID01360487 lag too short ID01360463 lag too short ID01360456 lag too short ID01360489 lag too short ID01002121 lag too short ID01002130 lag too short CM0000279777 lag too short CM0000278904 lag too short ID01370187 lag too short RE00033687 lag too short ID01230658 lag too short RE00032755 lag too short ID01202022 lag too short SMM_a10158860 lag too short SMM_a10128066 lag too short SMM_s20164694 lag too short SMM_s20083968 lag too short SMM_a10154853 lag too short SMM_a10125293 lag too short SMM_a10125292 lag too short SMM_a10125291 lag too short SMM_a10125290 lag too short SMM_a10127386 lag too short SMM_a10127384 lag too short SMM_a10127991 lag too short SMM_a10127992 lag too short SMM_a10128437 lag too short SMM_a10128438 lag too short SMM_a10015962 lag too short SMM_a10021656 lag too short SMM_a10015961 lag too short SMM_a10128064 lag too short SMM_a10018875 lag too short SMM_a10027112 lag too short SCI41184_37 lag too short SCI51312_214 lag too short SCI52439_214 lag too short SCI52446_214 lag too short SCI52435_214 lag too short SCI61468_201 lag too short SCI51712_214 lag too short SCI14960_25 lag too short SCI61501_201 lag too short SCI51334_214 lag too short SCI100695_384 lag too short SCI100696_384 lag too short SCI100995_383 lag too short SCI100996_383 lag too short SCI101295_194 lag too short SCI101296_194 lag too short SCI101595_384 lag too short SCI101596_384 lag too short SCI101895_383 lag too short SCI101896_383 lag too short SCI102195_194 lag too short SCI102196_194 lag too short SCI61488_201 lag too short SCI51016_214 lag too short SCI25734_9 lag too short SCI52371_214 lag too short SCI11781_353 lag too short SCI11791_353 lag too short SCI17702_353 lag too short SCI28628_353 lag too short SCI41187_37 lag too short SCI5843_353 lag too short SCI57576_201 lag too short SCI66684_15 lag too short SCI52427_214 lag too short SCI51346_214 lag too short SCI11607_25 lag too short SCI32215_25 lag too short SCI40976_9 lag too short SCI40977_9 lag too short SCI61419_201 lag too short SCI52343_214 lag too short SCI24198_37 lag too short SCI27954_37 lag too short SCI39277_37 lag too short SCI39321_37 lag too short SCI41058_37 lag too short SCI25331_15 lag too short SCI26692_15 lag too short SCI61418_201 lag too short SCI50777_214 lag too short SCI61404_201 lag too short SCI51408_214 lag too short SCI61421_201 lag too short SCI61406_201 lag too short SCI61405_201 lag too short SCI52436_214 lag too short SCI109723_214 lag too short SCI51273_214 lag too short SCI52250_214 lag too short SCI50976_214 lag too short SCI15681_37 lag too short SCI15692_37 lag too short SCI25865_37 lag too short SCI29155_37 lag too short SCI29156_37 lag too short SCI29161_37 lag too short SCI29163_37 lag too short SCI29164_37 lag too short SCI29165_37 lag too short SCI29166_37 lag too short SCI29167_37 lag too short SCI29188_37 lag too short SCI29189_37 lag too short SCI29191_37 lag too short SCI61395_201 lag too short SCI61439_201 lag too short SCI51017_214 lag too short SCI113685_99 lag too short SCI113684_99 lag too short SCI113469_310 lag too short SCI57570_201 lag too short SCI19561_331 lag too short SCI57578_201 lag too short SCI51208_214 lag too short SCI57574_201 lag too short SCI52429_214 lag too short SCI14956_330 lag too short SCI57575_201 lag too short SCI57725_201 lag too short SCI55611_214 lag too short SCI57587_201 lag too short SCI52428_214 lag too short SCI23248_330 lag too short SCI35929_330 lag too short SCI112726_113 lag too short SCI119367_108 lag too short SCI43299_108 lag too short SCI84744_108 lag too short SCI84752_108 lag too short SCI84754_108 lag too short SCI84756_108 lag too short SCI85466_108 lag too short SCI85467_108 lag too short SCI85510_108 lag too short SCI99469_108 lag too short SCI99470_108 lag too short SCI119265_310 lag too short SCI84757_194 lag too short SCI67429_37 lag too short SCI113413_164 lag too short SCI113414_164 lag too short SCI113415_164 lag too short SCI113418_164 lag too short SCI113420_164 lag too short SCI113421_164 lag too short SCI113422_164 lag too short SCI113423_164 lag too short SCI113424_164 lag too short SCI113425_164 lag too short SCI113428_164 lag too short SCI113429_164 lag too short SCI113430_164 lag too short SCI113431_164 lag too short SCI113432_164 lag too short SCI113433_164 lag too short SCI113434_164 lag too short SCI113435_164 lag too short SCI119401_88 lag too short SCI68399_330 lag too short SCI57400_441 lag too short SCI57501_440 lag too short SCI11867_453 lag too short SCI36350_453 lag too short SCI58067_441 lag too short SCI132221_37 lag too short SCI132222_37 lag too short SCI132237_37 lag too short SCI67873_331 lag too short SMM_a10154847 lag too short SMM_a10154843 lag too short SMM_a10154842 lag too short SMM_a10016993 lag too short SMM_a10127388 lag too short SMM_a10127389 lag too short SMM_a10127392 lag too short SMM_a10127391 lag too short SMM_a10127385 lag too short SMM_s20016673 lag too short SMM_s20016668 lag too short SMM_a10015815 lag too short SMM_a10015913 lag too short SMM_a10021668 lag too short SMM_a10021666 lag too short SMM_a10021667 lag too short SMM_a10017928 lag too short SMM_a10027110 lag too short SMM_a10027111 lag too short SMM_a10148273 lag too short SMM_a10148276 lag too short SMM_a10148279 lag too short