71

While working in Pandas in Python...

I'm working with a dataset that contains some missing values, and I'd like to return a dataframe which contains only those rows which have missing data. Is there a nice way to do this?

(My current method to do this is an inefficient "look to see what index isn't in the dataframe without the missing values, then make a df out of those indices.")

6 Answers6

137

You can use any axis=1 to check for least one True per row, then filter with boolean indexing:

null_data = df[df.isnull().any(axis=1)]
cs95
  • 379,657
  • 97
  • 704
  • 746
metersk
  • 11,803
  • 21
  • 63
  • 100
4
df.isnull().any(axis = 1).sum()

this gives you the total number of rows with at least one missing data

David Buck
  • 3,752
  • 35
  • 31
  • 35
Collins Kelechi
  • 235
  • 2
  • 3
2

If you want to see only the rows that contains the NaN values you could do:

data_frame[data_frame.iloc[:, insert column number here]=='NaN']
0

I just had this problem I assume you want to view a section of data frame made up of rows with missing values I used

df.loc[df.isnull().any(axis=1)]
Vinson Ciawandy
  • 996
  • 11
  • 26
agravaine
  • 1
  • 2
-1

You Can Use the code in this way

sum(df.isnull().any(axis=1))
-3

If you are looking for a quicker way to find the total number of missing rows in the dataframe, you can use this:

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

Ikay
  • 1