CODEWITHSUNDEEP
pythonpython-3.xpandas
joshi123
joshi123

Reputation: 865

pandas isnull sum with column headers

I have a dataframe which has multiple columns. I'd like to iterate through the columns, counting for each column how many null values there are and produce a new dataframe which displays the sum of isnull values alongside the column header names.

If I do: 

for col in main_df:
    print(sum(pd.isnull(data[col])))

I get a list of the null count for each column:

0
1
100

What I'm trying to do is create a new dataframe which has the column header alongside the null count, e.g. 

col1 | 0
col2 | 1
col3 | 100

Upvotes: 4

Views: 68498

Answers (3)

Skandar Mabrouk
Skandar Mabrouk

Reputation: 21

for a certain dataset you could use to show you the missing values and the number

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

Upvotes: 2

Pierre Delecto
Pierre Delecto

Reputation: 451

If the number of columns in your dataframe is greater than 10 you will end up with the middle columns being left out of the output. You can print every column using:

nulls = df.isnull().sum().to_frame()
for index, row in nulls.iterrows():
    print(index, row[0])

Upvotes: 2

MaxU - stand with Ukraine
MaxU - stand with Ukraine

Reputation: 210932

Try this:

In [71]: df
Out[71]:
     a    b  c
0  NaN  7.0  0
1  0.0  NaN  4
2  2.0  NaN  4
3  1.0  7.0  0
4  1.0  3.0  9
5  7.0  4.0  9
6  2.0  6.0  9
7  9.0  6.0  4
8  3.0  0.0  9
9  9.0  0.0  1

In [72]: pd.isnull(df).sum()
Out[72]:
a    1
b    2
c    0
dtype: int64

or:

In [76]: df.isnull().sum()
Out[76]:
a    1
b    2
c    0
dtype: int64

you can create a DF out of it:

In [78]: df.isnull().sum().to_frame('nulls')
Out[78]:
   nulls
a      1
b      2
c      0

Upvotes: 17

Related Questions