PYTHON

count missing values by column in pandas

df.isna().sum()

df count missing values

In [5]: df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})

In [6]: df.isna().sum()
Out[6]:
a    1
b    2
dtype: int64

number of columns with no missing values

null_cols = df.columns[df.isnull().all()]
df.drop(null_cols, axis = 1, inplace = True)

check for missing values by column in pandas

df.isna().any()

number of columns with no missing values

df = df[df.columns[~df.isnull().all()]]

find columns with missing values pandas

cols_with_missing = [col for col in X_train.columns
                     if X_train[col].isnull().any()]

pandas count number missing values

dfObj.isnull().sum()

check for missing values in pandas

df.isna()

how to check for missing values in pandas

dataframe.isnull()
dataframe.any()

pandas count number missing values

dfObj.isnull().sum().sum()

Number of missing values per column

country                   14
year                       0
uniqueid                   0
Has a Bank account        36
Type of Location          15
Cell Phone Access         11
household_size            28
Respondent Age            34
gender_of_respondent      34
The relathip with head     4
marital_status            32
Level of Educuation       29
Type of Job               30
dtype: int64

Count the number of Missing Values in the DataFrame

# Count the number of Missing Values in the DataFrame
df.isna().sum() 

Count the number of Non-Missing Values in the DataFrame

# Count the number of Non-Missing Values in the DataFrame
df.count()

getting the number of missing values in pandas

cols_to_delete = df.columns[df.isnull().sum()/len(df) > .90]
df.drop(cols_to_delete, axis = 1, inplace = True)