For循环不会在python中保存对csv文件的更改

Question

我正在尝试更改csv文件中的列，以便将日期和时间列合并为一个列。当我尝试在没有for循环的情况下使用特定的csv文件时，代码可以工作，但是当我尝试将代码放入循环的一部分时，它无法保存对csv文件的任何更改：

df = pd.read_csv('C:/Users/elsam/Documents/Year 3/Final EN3300 Project/Machine Learning/Data/locations/asp/row/noaa/data_id.csv')
pd.options.mode.chained_assignment = None
for f in [df, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12, df13, df14, df15, df16, df17, df18, df19, df20, df21, df23]:
    f['Date'] = f['Date'].astype(str)

    f = f.loc[(f['Date'] != '0')]

    f['year'] = f['Date'].str[:-4]
    f['month'] = f['Date'].str[-4:-2]
    f['day'] = f['Date'].str[-2:]

    f['time'] = f['time'].astype(str)

    f.loc[(f['time'] == '0'), 'time'] = '000'
    f.loc[(f['time'] == '2'), 'time'] = '002'
    f.loc[(f['time'] == '11'), 'time'] = '011'
    f.loc[(f['time'] == '20'), 'time'] = '020'
    f.loc[(f['time'] == '30'), 'time'] = '030'
    f.loc[(f['time'] == '40'), 'time'] = '040'
    f.loc[(f['time'] == '45'), 'time'] = '045'
    f.loc[(f['time'] == '46'), 'time'] = '046'
    f.loc[(f['time'] == '47'), 'time'] = '047'
    f.loc[(f['time'] == '53'), 'time'] = '053'
    f.loc[(f['time'] == '55'), 'time'] = '055'
    f.loc[(f['time'] == '56'), 'time'] = '056'
    f.loc[(f['time'] == '59'), 'time'] = '059'
    f['hour'] = f['time'].str[:-2]
    f['minute'] = f['time'].str[-2:]

    f['datetime'] = pd.to_datetime(f[['year', 'month', 'day', 'hour', 'minute']])
df.to_csv('C:/Users/elsam/Documents/Year 3/Final EN3300 Project/Machine Learning/Data/locations/asp/row/noaa/5 min NOAA.csv')

在for循环之前，我有一个要读取的csv文件列表，其格式与df csv文件的代码相同；在for循环之后，我有一个要保存的csv文件列表，其格式与df csv文件的代码相同，如上所示。但是，for循环中的任何更改似乎都没有保存到任何csv文件中。我想知道为什么这是因为当f被df、df2等替换时，代码在没有for循环的情况下工作

Answer 1

将您的代码更改为-

df = pd.read_csv('C:/Users/elsam/Documents/Year 3/Final EN3300 Project/Machine Learning/Data/locations/asp/row/noaa/data_id.csv')
pd.options.mode.chained_assignment = None
for i, f in enumerate([df, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12, df13, df14, df15, df16, df17, df18, df19, df20, df21, df23]):
    f['Date'] = f['Date'].astype(str)

    f = f.loc[(f['Date'] != '0')]

    f['year'] = f['Date'].str[:-4]
    f['month'] = f['Date'].str[-4:-2]
    f['day'] = f['Date'].str[-2:]

    f['time'] = f['time'].astype(str)

    f.loc[(f['time'] == '0'), 'time'] = '000'
    f.loc[(f['time'] == '2'), 'time'] = '002'
    f.loc[(f['time'] == '11'), 'time'] = '011'
    f.loc[(f['time'] == '20'), 'time'] = '020'
    f.loc[(f['time'] == '30'), 'time'] = '030'
    f.loc[(f['time'] == '40'), 'time'] = '040'
    f.loc[(f['time'] == '45'), 'time'] = '045'
    f.loc[(f['time'] == '46'), 'time'] = '046'
    f.loc[(f['time'] == '47'), 'time'] = '047'
    f.loc[(f['time'] == '53'), 'time'] = '053'
    f.loc[(f['time'] == '55'), 'time'] = '055'
    f.loc[(f['time'] == '56'), 'time'] = '056'
    f.loc[(f['time'] == '59'), 'time'] = '059'
    f['hour'] = f['time'].str[:-2]
    f['minute'] = f['time'].str[-2:]

    f['datetime'] = pd.to_datetime(f[['year', 'month', 'day', 'hour', 'minute']])
    f.to_csv('C:/Users/elsam/Documents/Year 3/Final EN3300 Project/Machine Learning/Data/locations/asp/row/noaa/5 min NOAA_' + str(i) + '_'.csv')