Reputation: 778

Pandas: print column name with missing values

I am trying to print or to get list of columns name with missing values. E.g.

data1 data2 data3  
1     3     3  
2     NaN   5  
3     4     NaN

I want to get ['data2', 'data3']. I wrote following code:

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.

Upvotes: 38

Answers (8)

leplandelaville

Reputation: 186

To get the names of the column names which does NOT have any missing values:

set(df.columns[df.isnull().mean()==0])

Upvotes: 0

M-sy Sy

Reputation: 1

df.columns[df.isnull().any()].index

Upvotes: 0

Gonçalo Peres

Reputation: 13602

For a dataframe df

missing = df.isnull().sum()
print(missing)

Upvotes: 0

Suhas_Pote

Reputation: 4590

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

Zero Values
Missing Values
% of Total Values
Total Zero Missing Values
% Total Zero Missing Values
Data Type

Just copy and paste following function and call it by passing your pandas Dataframe

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

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 %

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
'''

Upvotes: 14

Vinit Dawane

Reputation: 13

# 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)

Upvotes: 0

user2285236

Reputation:

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.columns[df.isnull().any()]

will return a list of the columns which have missing values.

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']

Upvotes: 113

piRSquared

Reputation: 294508

Another alternative:

df.loc[:, df.isnull().any()]

Upvotes: 8

Vedang Mehta

Reputation: 2254

Oneliner -

[col for col in df.columns if df[col].isnull().any()]

Upvotes: 18

Pandas: print column name with missing values

Answers (8)

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