Python 使用熊猫时，范围不是我所期望的

我想将

df

输入到一个函数中，将输出调用另一个数据帧'df_fn'，并使

df

保持不变。我该怎么做

我的代码要么什么都不做，要么

df_fn

等于

df

，要么

df_fn

和

df

都被修改

使用

df\u fn[cols][df\u fn.fuel\u type！=“gas”]=np.nan

import pandas as pd
import numpy as np

df = pd.DataFrame({'n_wheels': [2, 4, 4],
                   'color': ['red', 'blue', 'red'],
                   'year': [2010, 1990, 1999],
                   'fuel_type': ['diesel', 'gas', 'electric']})
print('df = \n', df)
def fn(df_fn):

    cols = ['n_wheels', 'color', 'year']
#     df_fn.loc[df_fn.fuel_type != 'gas', cols] = np.nan
    df_fn[cols][df_fn.fuel_type != 'gas'] = np.nan


    return df_fn

new_df = fn(df)
print('df = \n', df)
print('new_df = \n', new_df)

print('df = \n', df)
def fn(df_fn):

    cols = ['n_wheels', 'color', 'year']
#     df_fn[cols][df_fn.fuel_type != 'gas'] = np.nan
    df_fn.loc[df_fn.fuel_type != 'gas', cols] = np.nan


    return df_fn

new_df = fn(df)
print('df = \n', df)
print('new_df = \n', new_df)

输出：

df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric

df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric

new_df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric

df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric
df = 
    n_wheels color    year fuel_type
0       NaN   NaN     NaN    diesel
1       4.0  blue  1990.0       gas
2       NaN   NaN     NaN  electric
new_df = 
    n_wheels color    year fuel_type
0       NaN   NaN     NaN    diesel
1       4.0  blue  1990.0       gas
2       NaN   NaN     NaN  electric

使用

df_fn.loc[df_fn.fuel_type！='gas'，cols]=np.nan

import pandas as pd
import numpy as np

df = pd.DataFrame({'n_wheels': [2, 4, 4],
                   'color': ['red', 'blue', 'red'],
                   'year': [2010, 1990, 1999],
                   'fuel_type': ['diesel', 'gas', 'electric']})
print('df = \n', df)
def fn(df_fn):

    cols = ['n_wheels', 'color', 'year']
#     df_fn.loc[df_fn.fuel_type != 'gas', cols] = np.nan
    df_fn[cols][df_fn.fuel_type != 'gas'] = np.nan


    return df_fn

new_df = fn(df)
print('df = \n', df)
print('new_df = \n', new_df)

print('df = \n', df)
def fn(df_fn):

    cols = ['n_wheels', 'color', 'year']
#     df_fn[cols][df_fn.fuel_type != 'gas'] = np.nan
    df_fn.loc[df_fn.fuel_type != 'gas', cols] = np.nan


    return df_fn

new_df = fn(df)
print('df = \n', df)
print('new_df = \n', new_df)

输出：

df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric

df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric

new_df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric

df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric
df = 
    n_wheels color    year fuel_type
0       NaN   NaN     NaN    diesel
1       4.0  blue  1990.0       gas
2       NaN   NaN     NaN  electric
new_df = 
    n_wheels color    year fuel_type
0       NaN   NaN     NaN    diesel
1       4.0  blue  1990.0       gas
2       NaN   NaN     NaN  electric

---

您需要设置原始df的副本

print('df = \n', df)
def fn(df_fn):
    cols = ['n_wheels', 'color', 'year']
#     df_fn[cols][df_fn.fuel_type != 'gas'] = np.nan
    df_fn.loc[df_fn.fuel_type != 'gas', cols] = np.nan
    return df_fn
df1=df.copy()#I change here add copy 
new_df = fn(df1)
print('df = \n', df)
print('new_df = \n', new_df)
df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric
df = 
    n_wheels color  year fuel_type
0         2   red  2010    diesel
1         4  blue  1990       gas
2         4   red  1999  electric
new_df = 
    n_wheels color    year fuel_type
0       NaN   NaN     NaN    diesel
1       4.0  blue  1990.0       gas
2       NaN   NaN     NaN  electric

---

哦，是的，这很有效。你能解释一下为什么我的两个版本不起作用吗？我不应该因为作用域是如何工作的而不需要复制吗？@vivian first one如果你在函数之外运行它，就会返回一个警告

df[cols][df.fuel\u type！='gas']=np.nan

try，第二个会在没有复制的情况下重写原始df