How do I get a summary count of missing/NaN data by column in 'pandas'?

Question

In R I can quickly see a count of missing data using the summary command, but the equivalent pandas DataFrame method, describe does not report these values.

I gather I can do something like

len(mydata.index) - mydata.count()

to compute the number of missing values for each column, but I wonder if there's a better idiom (or if my approach is even right).

Answer 1

Both describe and info report the count of non-missing values.

In [1]: df = DataFrame(np.random.randn(10,2))

In [2]: df.iloc[3:6,0] = np.nan

In [3]: df
Out[3]: 
          0         1
0 -0.560342  1.862640
1 -1.237742  0.596384
2  0.603539 -1.561594
3       NaN  3.018954
4       NaN -0.046759
5       NaN  0.480158
6  0.113200 -0.911159
7  0.990895  0.612990
8  0.668534 -0.701769
9 -0.607247 -0.489427

[10 rows x 2 columns]

In [4]: df.describe()
Out[4]: 
              0          1
count  7.000000  10.000000
mean  -0.004166   0.286042
std    0.818586   1.363422
min   -1.237742  -1.561594
25%   -0.583795  -0.648684
50%    0.113200   0.216699
75%    0.636036   0.608839
max    0.990895   3.018954

[8 rows x 2 columns]


In [5]: df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 10 entries, 0 to 9
Data columns (total 2 columns):
0    7 non-null float64
1    10 non-null float64
dtypes: float64(2)

To get a count of missing, your soln is correct

In [20]: len(df.index)-df.count()
Out[20]: 
0    3
1    0
dtype: int64

You could do this too

In [23]: df.isnull().sum()
Out[23]: 
0    3
1    0
dtype: int64

Answer 2

As a tiny addition, to get percentage missing by DataFrame column, combining @Jeff and @userS's answers above gets you:

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

Answer 3

This isnt quite a full summary, but it will give you a quick sense of your column level data

def getPctMissing(series):
    num = series.isnull().sum()
    den = series.count()
    return 100*(num/den)

Answer 4

Following one will do the trick and will return counts of nulls for every column:

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

df.isnull() returns a dataframe with True / False values
sum(axis=0) sums the values across all rows for a column

Answer 5

If you want to see not null summary of each column , just use df.info(null_counts=True):

Example 1:

df = pd.DataFrame(np.random.randn(10,5), columns=list('abcde'))
df.iloc[:4,0] = np.nan
df.iloc[:3,1] = np.nan
df.iloc[:2,2] = np.nan
df.iloc[:1,3] = np.nan

df.info(null_counts=True)

output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 5 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   a       6 non-null      float64
 1   b       7 non-null      float64
 2   c       8 non-null      float64
 3   d       9 non-null      float64
 4   e       10 non-null     float64
dtypes: float64(5)
memory usage: 528.0 bytes

In addition, if you want to customize the result , such as add nan_rate , I wrote a method

def describe_nan(df):
    return pd.DataFrame([(i, df[df[i].isna()].shape[0],df[df[i].isna()].shape[0]/df.shape[0]) for i in df.columns], columns=['column', 'nan_counts', 'nan_rate'])

describe_nan(df)

>>> column  nan_counts  nan_rate
0   a   4   0.4
1   b   3   0.3
2   c   2   0.2
3   d   1   0.1
4   e   0   0.0

Answer 6

If you didn't care which columns had Nan's and you just wanted to check overall, just add a second .sum() to get a single value.

result = df.isnull().sum().sum()
result > 0

a Series would only need one .sum() and a Panel() would need three