Find empty or NaN entry in Pandas Dataframe - list

Question

I am trying to search through a Pandas Dataframe to find where it has a missing entry or a NaN entry.
Here is a dataframe that I am working with:
cl_id a c d e A1 A2 A3
0 1 -0.419279 0.843832 -0.530827 text76 1.537177 -0.271042
1 2 0.581566 2.257544 0.440485 dafN_6 0.144228 2.362259
2 3 -1.259333 1.074986 1.834653 system 1.100353
3 4 -1.279785 0.272977 0.197011 Fifty -0.031721 1.434273
4 5 0.578348 0.595515 0.553483 channel 0.640708 0.649132
5 6 -1.549588 -0.198588 0.373476 audio -0.508501
6 7 0.172863 1.874987 1.405923 Twenty NaN NaN
7 8 -0.149630 -0.502117 0.315323 file_max NaN NaN
NOTE: The blank entries are empty strings - this is because there was no alphanumeric content in the file that the dataframe came from.
If I have this dataframe, how can I find a list with the indexes where the NaN or blank entry occurs?

Answer 1

np.where(pd.isnull(df)) returns the row and column indices where the value is NaN:
In [152]: import numpy as np
In [153]: import pandas as pd
In [154]: np.where(pd.isnull(df))
Out[154]: (array([2, 5, 6, 6, 7, 7]), array([7, 7, 6, 7, 6, 7]))
In [155]: df.iloc[2,7]
Out[155]: nan
In [160]: [df.iloc[i,j] for i,j in zip(*np.where(pd.isnull(df)))]
Out[160]: [nan, nan, nan, nan, nan, nan]
Finding values which are empty strings could be done with applymap:
In [182]: np.where(df.applymap(lambda x: x == ''))
Out[182]: (array([5]), array([7]))
Note that using applymap requires calling a Python function once for each cell of the DataFrame. That could be slow for a large DataFrame, so it would be better if you could arrange for all the blank cells to contain NaN instead so you could use pd.isnull.

Answer 2

Try this:
df[df['column_name'] == ''].index
and for NaNs you can try:
pd.isna(df['column_name'])

Answer 3

Check if the columns contain Nan using .isnull() and check for empty strings using .eq(''), then join the two together using the bitwise OR operator |.
Sum along axis 0 to find columns with missing data, then sum along axis 1 to the index locations for rows with missing data.
missing_cols, missing_rows = (
(df2.isnull().sum(x) | df2.eq('').sum(x))
.loc[lambda x: x.gt(0)].index
for x in (0, 1)
)
>>> df2.loc[missing_rows, missing_cols]
A2 A3
2 1.10035
5 -0.508501
6 NaN NaN
7 NaN NaN

Answer 4

I've resorted to
df[ (df[column_name].notnull()) & (df[column_name]!=u'') ].index
lately. That gets both null and empty-string cells in one go.

Answer 5

In my opinion, don't waste time and just replace with NaN! Then, search all entries with Na. (This is correct because empty values are missing values anyway).
import numpy as np # to use np.nan
import pandas as pd # to use replace
df = df.replace(' ', np.nan) # to get rid of empty values
nan_values = df[df.isna().any(axis=1)] # to get all rows with Na
nan_values # view df with NaN rows only

Answer 6

Partial solution: for a single string column
tmp = df['A1'].fillna(''); isEmpty = tmp==''
gives boolean Series of True where there are empty strings or NaN values.

Answer 7

you also do something good:
text_empty = df['column name'].str.len() > -1
df.loc[text_empty].index
The results will be the rows which are empty & it's index number.

Answer 8

Another opltion covering cases where there might be severar spaces is by using the isspace() python function.
df[df.col_name.apply(lambda x:x.isspace() == False)] # will only return cases without empty spaces
adding NaN values:
df[(df.col_name.apply(lambda x:x.isspace() == False) & (~df.col_name.isna())]

Answer 9

To obtain all the rows that contains an empty cell in in a particular column.
DF_new_row=DF_raw.loc[DF_raw['columnname']=='']
This will give the subset of DF_raw, which satisfy the checking condition.

Answer 10

You can use string methods with regex to find cells with empty strings:
df[~df.column_name.str.contains('\w')].column_name.count()