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This article mainly explains "how to count the missing values of a column or row by pandas". Interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how pandas counts the missing values of a column or row.

Count the number of missing values in a column or row. Use isnull () import pandas as pd# to first import data df = pd.read_csv ('123.csv', encoding='gbk') # to calculate the number of missing values in each row of data That is, the missing value rows_null = df.isnull (). Sum (axis=1) # is the missing value col_null = df.isnull (). Sum (axis=0) # counts the missing value of the entire df all_null = df.isnull (). Sum (). Sum () # counts the missing value of a column idx_null = df ['column name'] .isnull (). Sum (axis=0) 2. Use countimport pandas as pd# to first import data df = pd.read_csv ('123.csv', encoding='gbk') # to calculate how many non-empty values each row of data has That is, non-null values by row rows_not_null = df.count (axis=1) # the following are column-by-column non-null values cols_not_null = df.count (axis=0) cols_null = df.shape [1]-cols_not_null# non-null values col_not_null = df ['column name'] .count (axis=0) use pandas to deal with missing values def missing_values (dataframe): missing_ratio = ( Dataframe.isnull (). Sum () / len (dataframe) * 100 missing_ratio = missing_ratio.drop (missing_ ratio [missing _ ratio = = 0] .index). Sort_values (ascending=False) missing_count = dataframe.isnull (). Sum () missing_count = missing_count.drop (missing_ count [missing _ count = = 0] .index). Sort_values (ascending=False) info = pd.DataFrame ({'Missing Ratio': missing_ratio) 'Missing Count': missing_count}) return info to this point I believe you have a deeper understanding of "how pandas counts the missing values of a column or row". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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