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This article mainly analyzes the relevant knowledge of what is the method for pandas to return the missing value position, the content is detailed and easy to understand, the operation details are reasonable, and has a certain reference value. If you are interested, you might as well follow the editor to have a look, and follow the editor to learn more about "what is the way for pandas to return missing values?"

Pandas returns the position of missing values

Sometimes we may need to get some information about missing values, so we need to get the location of these missing values in the DataFrame.

If the index of our DataFrame is a numeric sequential index, we want to return the position of the missing value

Import numpy as npimport pandas as pd

Let's first build a DataFrame with missing values

Df = pd.DataFrame ({'Aids: [1, 2, 3, np.nan],' np.nan: [2, np.nan, 4, 6], 'np.nan: [3, 2, np.nan, 3],' dating: [np.nan, 1, np.nan, 2]}) display (df)

First we can see if there are any missing values in each column

Df.isnull () .any ()

We find that all four columns have missing values. For example, we want to know the exact location of the missing values in each column. Take An as an example.

Df.isnull () .any ()

You can see the index that returns the location of the missing value of A, so that when locating, you can use the loc function to locate the missing value directly for easy viewing.

If we want to generate a dictionary to store all the missing value location information, we can use:

A = df.isnull () .any () a = a.loco [a = = True] columns = a.index.tolist () mydict = {} # create a dictionary to store all locations temp = [] for column in columns: temp = df.loc.df [column] .isnull () .index.ist () mydict [column] = tempmydict

Attached: Pandas looks up the location of the missing value And return the missing value line number and column number #! / usr/bin/evn python#-*-coding:utf8-*-import pandas as pddata = [[None, None, 90, 80], [57, 43, 89, 65], [78, 50, 67, 78], [67, 45, 78, 76], [77, 88, None, 45], [52, 110, 120, 99], [131,13, 32, 12]] index = ['language', 'English'. Mathematics, Politics, Physics, Chemistry, Biology, Geography] column = ['Zhang San','Li Si', 'Wang Wu', 'Saturday'] data = pd.DataFrame (data,index=index Columns=column) print (data) print ("=\ n") for columname in data.columns: if data [columname] .count ()! = len (data): loc = data [columname] [data [columnname] .isnull () .values = = True] .index.tolist () print ('column name: "{}", line {} has missing value' .format (columname,loc))

Execution result

F:\ Opensources\ python\ python.exe D:/pythonStudy/EXCELDB/LagelangriCZ_test.py

Zhang San Li Si Wang Wu Saturday

Chinese NaN NaN 90.080

English 57.0 43.0 89.0 65

Mathematics 78.0 50.0 67.0 78

Political NaN 78.090.073

Physics 67.0 45.0 78.0 76

Chemistry 77.088.0 NaN 45

Biology 52.0 110.0 120.0 99

Geography 131.0 13.0 32.0 12

=

List: "Zhang San", there is a missing value in line ['Chinese', 'politics'].

List: "Li Si", there is a missing value in line ['Chinese'].

List: "Wang Wu", there is a missing value in line ['chemistry'].

Process finished with exit code 0

This article mainly analyzes the relevant knowledge of what is the method for pandas to return the missing value position, the content is detailed and easy to understand, the operation details are reasonable, and has a certain reference value. If you are interested, you might as well follow the editor to have a look, and follow the editor to learn more about "what is the way for pandas to return missing values?"

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