AttributeError：“ DataFrame”对象没有属性“ tolist”

Question

当我在Jupyter Notebook中运行以下代码时：

columns = ['nkill', 'nkillus', 'nkillter','nwound', 'nwoundus', 'nwoundte', 'propvalue', 'nperps', 'nperpcap', 'iyear', 'imonth', 'iday']

for col in columns:
    # needed for any missing values set to '-99'
    df[col] = [np.nan if (x < 0) else x for x in 
df[col].tolist()]
    # calculate the mean of the column
    column_temp = [0 if math.isnan(x) else x for x in df[col].tolist()]
    mean = round(np.mean(column_temp))
    # then apply the mean to all NaNs
    df[col].fillna(mean, inplace=True)

我收到以下错误：

AttributeError                            Traceback 
(most recent call last)
<ipython-input-56-f8a0a0f314e6> in <module>()
  3 for col in columns:
  4     # needed for any missing values set to '-99'
----> 5     df[col] = [np.nan if (x < 0) else x for x in df[col].tolist()]
  6     # calculate the mean of the column
  7     column_temp = [0 if math.isnan(x) else x for x in df[col].tolist()]

/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in __getattr__(self, name)
   4374             if self._info_axis._can_hold_identifiers_and_holds_name(name):
   4375                 return self[name]
-> 4376             return object.__getattribute__(self, name)
   4377 
   4378     def __setattr__(self, name, value):

AttributeError: 'DataFrame' object has no attribute 'tolist'

当我在Pycharm中运行该代码时，它可以正常工作，而我的所有研究都使我得出结论：它应该很好。我想念什么吗？

我在下面创建了一个最小，完整和可验证的示例：

import numpy as np
import pandas as pd
import os
import math

# get the path to the current working directory
cwd = os.getcwd()

# then add the name of the Excel file, including its extension to get its relative path
# Note: make sure the Excel file is stored inside the cwd
file_path = cwd + "/data.xlsx"

# Copy the database to file
df = pd.read_excel(file_path)

columns = ['nkill', 'nkillus', 'nkillter', 'nwound', 'nwoundus', 'nwoundte', 'propvalue', 'nperps', 'nperpcap', 'iyear', 'imonth', 'iday']

for col in columns:
    # needed for any missing values set to '-99'
    df[col] = [np.nan if (x < 0) else x for x in df[col].tolist()]
    # calculate the mean of the column
    column_temp = [0 if math.isnan(x) else x for x in df[col].tolist()]
    mean = round(np.mean(column_temp))
    # then apply the mean to all NaNs
    df[col].fillna(mean, inplace=True)

Answer 1

您有一个XY Problem。您已经在评论中描述了您要实现的目标，但是您的方法不适用于熊猫。

避免for循环和list

对于Pandas，您应该避免显式的for循环或转换为Python list。 Pandas建立在NumPy数组的基础上，该数组支持矢量化列式操作。

因此，让我们看一下如何重写：

for col in columns:
    # values less than 0 set to NaN
    # calculate the mean of the column with 0 for NaN
    # then apply the mean to all NaNs

您现在可以使用Pandas方法来实现上述目标。

apply + pd.to_numeric + mask + fillna

您可以定义函数mean_update并使用pd.DataFrame.apply将其应用于每个系列：

df = pd.DataFrame({'A': [1, -2, 3, np.nan],
                   'B': ['hello', 4, 5, np.nan],
                   'C': [-1.5, 3, np.nan, np.nan]})

def mean_update(s):
    s_num = pd.to_numeric(s, errors='coerce')  # convert to numeric
    s_num = s_num.mask(s_num < 0)              # replace values less than 0 with NaN
    s_mean = s_num.fillna(0).mean()            # calculate mean
    return s_num.fillna(s_mean)                # replace NaN with mean

df = df.apply(mean_update)                     # apply to each series

print(df)

     A     B     C
0  1.0  2.25  0.75
1  1.0  4.00  3.00
2  3.0  5.00  0.75
3  1.0  2.25  0.75