Python Pandas：打印缺少值的列名
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Pandas: print column name with missing values
提问by LinearLeopard
I am trying to print or to get list of columns name with missing values. E.g.
我正在尝试打印或获取缺少值的列名称列表。例如
data1 data2 data3
1 3 3
2 NaN 5
3 4 NaN
I want to get ['data2', 'data3']. I wrote following code:
我想得到 ['data2', 'data3']。我写了以下代码：
print('\n'.join(map(
lambda x : str(x[1])
,(filter(lambda z: z[0] != False, zip(train.isnull().any(axis=0), train.columns.values)))
)))
It works well, but I think should be simpler way.
它运作良好，但我认为应该是更简单的方法。
回答by ayhan
df.isnull().any()generates a boolean array (True if the column has a missing value, False otherwise). You can use it to index into df.columns:
df.isnull().any()生成一个布尔数组（如果列有缺失值则为真，否则为假）。您可以使用它来索引df.columns：
df.columns[df.isnull().any()]
will return a list of the columns which have missing values.
将返回具有缺失值的列的列表。
df = pd.DataFrame({'A': [1, 2, 3],
'B': [1, 2, np.nan],
'C': [4, 5, 6],
'D': [np.nan, np.nan, np.nan]})
df
Out:
A B C D
0 1 1.0 4 NaN
1 2 2.0 5 NaN
2 3 NaN 6 NaN
df.columns[df.isnull().any()]
Out: Index(['B', 'D'], dtype='object')
df.columns[df.isnull().any()].tolist() # to get a list instead of an Index object
Out: ['B', 'D']
回答by Vedang Mehta
Oneliner -
单线 -
[col for col in df.columns if df[col].isnull().any()]
回答by piRSquared
Another alternative:
另一种选择：
df.loc[:, df.isnull().any()]
回答by Suhas_Pote
import numpy as np
import pandas as pd
raw_data = {'first_name': ['Jason', np.nan, 'Tina', 'Jake', 'Amy'],
'last_name': ['Miller', np.nan, np.nan, 'Milner', 'Cooze'],
'age': [22, np.nan, 23, 24, 25],
'sex': ['m', np.nan, 'f', 'm', 'f'],
'Test1_Score': [4, np.nan, 0, 0, 0],
'Test2_Score': [25, np.nan, np.nan, 0, 0]}
results = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'sex', 'Test1_Score', 'Test2_Score'])
results
'''
first_name last_name age sex Test1_Score Test2_Score
0 Jason Miller 22.0 m 4.0 25.0
1 NaN NaN NaN NaN NaN NaN
2 Tina NaN 23.0 f 0.0 NaN
3 Jake Milner 24.0 m 0.0 0.0
4 Amy Cooze 25.0 f 0.0 0.0
'''
You can use following function, which will give you output in Dataframe
您可以使用以下功能，它将在数据框中为您提供输出
- Zero Values
- Missing Values
- % of Total Values
- Total Zero Missing Values
- % Total Zero Missing Values
- Data Type
- 零值
- 缺失值
- 占总价值的百分比
- 总零缺失值
- % 总零缺失值
- 数据类型
Just copy and paste following function and call it by passing your pandas Dataframe
只需复制并粘贴以下函数并通过传递您的熊猫数据框来调用它
def missing_zero_values_table(df):
zero_val = (df == 0.00).astype(int).sum(axis=0)
mis_val = df.isnull().sum()
mis_val_percent = 100 * df.isnull().sum() / len(df)
mz_table = pd.concat([zero_val, mis_val, mis_val_percent], axis=1)
mz_table = mz_table.rename(
columns = {0 : 'Zero Values', 1 : 'Missing Values', 2 : '% of Total Values'})
mz_table['Total Zero Missing Values'] = mz_table['Zero Values'] + mz_table['Missing Values']
mz_table['% Total Zero Missing Values'] = 100 * mz_table['Total Zero Missing Values'] / len(df)
mz_table['Data Type'] = df.dtypes
mz_table = mz_table[
mz_table.iloc[:,1] != 0].sort_values(
'% of Total Values', ascending=False).round(1)
print ("Your selected dataframe has " + str(df.shape[1]) + " columns and " + str(df.shape[0]) + " Rows.\n"
"There are " + str(mz_table.shape[0]) +
" columns that have missing values.")
# mz_table.to_excel('D:/sampledata/missing_and_zero_values.xlsx', freeze_panes=(1,0), index = False)
return mz_table
missing_zero_values_table(results)
Output
输出
Your selected dataframe has 6 columns and 5 Rows.
There are 6 columns that have missing values.
Zero Values Missing Values % of Total Values Total Zero Missing Values % Total Zero Missing Values Data Type
last_name 0 2 40.0 2 40.0 object
Test2_Score 2 2 40.0 4 80.0 float64
first_name 0 1 20.0 1 20.0 object
age 0 1 20.0 1 20.0 float64
sex 0 1 20.0 1 20.0 object
Test1_Score 3 1 20.0 4 80.0 float64
If you want to keep it simple then you can use following function to get missing values in %
如果你想保持简单，那么你可以使用以下函数来获取 % 中的缺失值
def missing(dff):
print (round((dff.isnull().sum() * 100/ len(dff)),2).sort_values(ascending=False))
missing(results)
'''
Test2_Score 40.0
last_name 40.0
Test1_Score 20.0
sex 20.0
age 20.0
first_name 20.0
dtype: float64
'''
回答by Vinit Dawane
# Developing a loop to identify and remove columns where more than 50% of the values are missing#
i = 0
count_of_columns_removed = 0
a = np.array([50,60,70,80,90,100])
percent_NA = 0
for i in app2.columns:
percent_NA = round(100*(app2[i].isnull().sum()/len(app2.index)),2)
# Replace app2 with relevant name
if percent_NA >= a.all():
print(i)
app2 = app2.drop(columns=i)
count_of_columns_removed += 1
print(count_of_columns_removed)
