我正在尝试打印或获取具有缺失值的列名列表。例如。
Private Sub cbExtensão_SelectedIndexChanged(sender As Object, e As EventArgs) Handles cbExtensão.SelectedIndexChanged
If rbPorData.Checked Then
DataGridView2.DataSource = FillDataGridViewData(cbExtensão.Text)
ElseIf rbPorExtensão.Checked Then
DataGridView2.DataSource = FillDataGridViewExtensao(cbExtensão.Text)
ElseIf rbPorNome.Checked Then
DataGridView2.DataSource = FillDataGridViewName(cbExtensão.Text)
Else
MsgBox("Please, check an option to search")
End If
End Sub
我想获得[' data2',' data3']。 我写了以下代码:
Else
效果很好,但我认为应该更简单。
答案 0 :(得分:36)
df.isnull().any()
生成一个布尔数组(如果列有缺失值,则返回True,否则返回False)。您可以使用它来索引df.columns
:
df.columns[df.isnull().any()]
将返回缺少值的列的列表。
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']
答案 1 :(得分:7)
Oneliner -
[col for col in df.columns if df[col].isnull().any()]
答案 2 :(得分:3)
另一种选择:
df.loc[:, df.isnull().any()]
答案 3 :(得分:0)
# 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)
答案 4 :(得分:0)
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
'''
您可以使用以下功能,该功能将在Dataframe中提供输出
只需复制并粘贴以下函数,然后通过传递您的pandas Dataframe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)
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
如果要保持简单,则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
'''
答案 5 :(得分:0)
对于数据框df
missing = df.isnull().sum()
print(missing)
答案 6 :(得分:0)
df.columns[df.isnull().any()].index
答案 7 :(得分:0)
要获取没有任何缺失值的列名的名称:
set(df.columns[df.isnull().mean()==0])