How to count the NaN values in a column in pandas DataFrame

Use the isna() method (or it's alias isnull() which is also compatible with older pandas versions < 0.21.0) and then sum to count the NaN values. For one column:

>>> s = pd.Series([1,2,3, np.nan, np.nan])

>>> s.isna().sum()   # or s.isnull().sum() for older pandas versions
2

For several columns, this also works:

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

>>> df.isna().sum()
a    1
b    2
dtype: int64

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Lets assume df is a pandas DataFrame.

Then,

df.isnull().sum(axis = 0)

This will give number of NaN values in every column.

If you need, NaN values in every row,

df.isnull().sum(axis = 1)

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You could subtract the total length from the count of non-nan values:

count_nan = len(df) - df.count()

You should time it on your data. For small Series got a 3x speed up in comparison with the isnull solution.

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Based on the most voted answer we can easily define a function that gives us a dataframe to preview the missing values and the % of missing values in each column:

def missing_values_table(df):
    mis_val = df.isnull().sum()
    mis_val_percent = 100 * df.isnull().sum() / len(df)
    mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)
    mis_val_table_ren_columns = mis_val_table.rename(
    columns = {0 : 'Missing Values', 1 : '% of Total Values'})
    mis_val_table_ren_columns = mis_val_table_ren_columns[
        mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(
    '% of Total Values', ascending=False).round(1)
    print ("Your selected dataframe has " + str(df.shape[1]) + " columns.\n"      
        "There are " + str(mis_val_table_ren_columns.shape[0]) +
            " columns that have missing values.")
    return mis_val_table_ren_columns

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Since pandas 0.14.1 my suggestion here to have a keyword argument in the value_counts method has been implemented:

import pandas as pd
df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]})
for col in df:
    print df[col].value_counts(dropna=False)

2     1
 1     1
NaN    1
dtype: int64
NaN    2
 1     1
dtype: int64

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if its just counting nan values in a pandas column here is a quick way

import pandas as pd
## df1 as an example data frame 
## col1 name of column for which you want to calculate the nan values
sum(pd.isnull(df1['col1']))

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The below will print all the Nan columns in descending order.

df.isnull().sum().sort_values(ascending = False)

or

The below will print first 15 Nan columns in descending order.

df.isnull().sum().sort_values(ascending = False).head(15)

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df.isnull().sum() will give the column-wise sum of missing values.

If you want to know the sum of missing values in a particular column then following code will work: df.column.isnull().sum()

---

df.isnull().sum() 
      //type: <class 'pandas.core.series.Series'>

or

df.column_name.isnull().sum()
     //type: <type 'numpy.int64'>

---

if you are using Jupyter Notebook, How about....

 %%timeit
 df.isnull().any().any()

or

 %timeit 
 df.isnull().values.sum()

or, are there anywhere NaNs in the data, if yes, where?

 df.isnull().any()

---

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
'''

---

Please use below for particular column count

dataframe.columnName.isnull().sum()

---

To count zeroes:

df[df == 0].count(axis=0)

To count NaN:

df.isnull().sum()

or

df.isna().sum()

---

Hope this helps,

import pandas as pd
import numpy as np
df = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan],'c':[np.nan,2,np.nan], 'd':[np.nan,np.nan,np.nan]})

https://i.stack.imgur.com/f32tg.png

df.isnull().sum()/len(df) * 100

https://i.stack.imgur.com/jYryw.png

Thres = 40
(df.isnull().sum()/len(df) * 100 ) < Thres

https://i.stack.imgur.com/C3X3t.png

---

You can use value_counts method and print values of np.nan

s.value_counts(dropna = False)[np.nan]

---

One other simple option not suggested yet, to just count NaNs, would be adding in the shape to return the number of rows with NaN.

df[df['col_name'].isnull()]['col_name'].shape

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For the 1st part count NaN we have multiple way.

Method 1 count , due to the count will ignore the NaN which is different from size

print(len(df) - df.count())

Method 2 isnull / isna chain with sum

print(df.isnull().sum())
#print(df.isna().sum())

Method 3 describe / info : notice this will output the 'notnull' value count

print(df.describe())
#print(df.info())

Method from numpy

print(np.count_nonzero(np.isnan(df.values),axis=0))

For the 2nd part of the question, If we would like drop the column by the thresh,we can try with dropna

thresh, optional Require that many non-NA values.

Thresh = n # no null value require, you can also get the by int(x% * len(df))
df = df.dropna(thresh = Thresh, axis = 1)

---

df1.isnull().sum()

This will do the trick.

---

Here is the code for counting Null values column wise :

df.isna().sum()

---

There is a nice Dzone article from July 2017 which details various ways of summarising NaN values. Check it out here.

The article I have cited provides additional value by: (1) Showing a way to count and display NaN counts for every column so that one can easily decide whether or not to discard those columns and (2) Demonstrating a way to select those rows in specific which have NaNs so that they may be selectively discarded or imputed.

Here's a quick example to demonstrate the utility of the approach - with only a few columns perhaps its usefulness is not obvious but I found it to be of help for larger data-frames.

import pandas as pd
import numpy as np

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

# Check whether there are null values in columns
null_columns = df.columns[df.isnull().any()]
print(df[null_columns].isnull().sum())

# One can follow along further per the cited article

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In case you need to get the non-NA (non-None) and NA (None) counts across different groups pulled out by groupby:

gdf = df.groupby(['ColumnToGroupBy'])

def countna(x):
    return (x.isna()).sum()

gdf.agg(['count', countna, 'size'])

This returns the counts of non-NA, NA and total number of entries per group.

---

You can try with:

In [1]: s = pd.DataFrame('a'=[1,2,5, np.nan, np.nan,3],'b'=[1,3, np.nan, np.nan,3,np.nan])

In [4]: s.isna().sum()   
Out[4]: out = {'a'=2, 'b'=3} # the number of NaN values for each column

If needed the gran total of nans:

In [5]: s.isna().sum().sum()
Out[6]: out = 5  #the inline sum of Out[4] 

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based to the answer that was given and some improvements this is my approach

def PercentageMissin(Dataset):
    """this function will return the percentage of missing values in a dataset """
    if isinstance(Dataset,pd.DataFrame):
        adict={} #a dictionary conatin keys columns names and values percentage of missin value in the columns
        for col in Dataset.columns:
            adict[col]=(np.count_nonzero(Dataset[col].isnull())*100)/len(Dataset[col])
        return pd.DataFrame(adict,index=['% of missing'],columns=adict.keys())
    else:
        raise TypeError("can only be used with panda dataframe")

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I use this loop to count missing values for each column:

# check missing values
import numpy as np, pandas as pd
for col in df:
      print(col +': '+ np.str(df[col].isna().sum()))

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You can use df.iteritems() to loop over the data frame. Set a conditional within a for loop to calculate the NaN values percent for each column, and drop those that contain a value of NaNs over your set threshold:

for col, val in df.iteritems():
    if (df[col].isnull().sum() / len(val) * 100) > 30:
        df.drop(columns=col, inplace=True)

---

Used the solution proposed by @sushmit in my code.

A possible variation of the same can also be

colNullCnt = []
for z in range(len(df1.cols)):
    colNullCnt.append([df1.cols[z], sum(pd.isnull(trainPd[df1.cols[z]]))])

Advantage of this is that it returns the result for each of the columns in the df henceforth.

---

import pandas as pd
import numpy as np

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

# count the NaNs in a column
num_nan_a = df.loc[ (pd.isna(df['a'])) , 'a' ].shape[0]
num_nan_b = df.loc[ (pd.isna(df['b'])) , 'b' ].shape[0]

# summarize the num_nan_b
print(df)
print(' ')
print(f"There are {num_nan_a} NaNs in column a")
print(f"There are {num_nan_b} NaNs in column b")

Gives as output:

     a    b
0  1.0  NaN
1  2.0  1.0
2  NaN  NaN

There are 1 NaNs in column a
There are 2 NaNs in column b

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Suppose you want to get the number of missing values(NaN) in a column(series) known as price in a dataframe called reviews

#import the dataframe
import pandas as pd

reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)

To get the missing values, with n_missing_prices as the variable, simple do

n_missing_prices = sum(reviews.price.isnull())
print(n_missing_prices)

sum is the key method here, was trying to use count before i realized sum is the right method to use in this context

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I've written a short function (Python 3) to produce .info as a pandas dataframe that can be then be written to excel:

df1 = pd.DataFrame({'a':[1,2,np.nan], 'b':[np.nan,1,np.nan]}) 
def info_as_df (df):
    null_counts = df.isna().sum()
    info_df = pd.DataFrame(list(zip(null_counts.index,null_counts.values))\
                                         , columns = ['Column', 'Nulls_Count'])
    data_types = df.dtypes
    info_df['Dtype'] = data_types.values
    return info_df
print(df1.info())
print(info_as_df(df1))

Which gives:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   a       2 non-null      float64
 1   b       1 non-null      float64
dtypes: float64(2)
memory usage: 176.0 bytes
None
  Column  Nulls_Count    Dtype
0      a            1  float64
1      b            2  float64

---

Another way just for completeness is using np.count_nonzero with .isna():

np.count_nonzero(df.isna())

%timeit np.count_nonzero(df.isna())
512 ms ± 24.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Comparing with the top answers using 1000005 rows × 16 columns dataframe:

%timeit df.isna().sum()
492 ms ± 55.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit df.isnull().sum(axis = 0)
478 ms ± 34.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit count_nan = len(df) - df.count()
484 ms ± 47.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

data:

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'])

# big dataframe for %timeit 
big_df = pd.DataFrame(np.random.randint(0, 100, size=(1000000, 10)), columns=list('ABCDEFGHIJ'))
df = pd.concat([big_df,results]) # 1000005 rows × 16 columns

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