pandas: remove rows with missing data

Question

I am using the following code to remove some rows with missing data in pandas:

df = df.replace(r'^\s+$', np.nan, regex=True)
df = df.replace(r'^\t+$', np.nan, regex=True)
df = df.dropna()

However, I still have some cells in the data frame looks blank/empty. Why is this happening? Any way to get rid of rows with such empty/blank cells? Thanks!

Can you show us samples of the dataframe so that we can reproduce the problems — Sreeram TP, Aug 22 '18 at 05:30
Like @jezrael said, try adding `df = df.replace('', np.nan, regex=True)` before `dropna` in your code — Sreeram TP, Aug 22 '18 at 05:31
Possible duplicate of [Python Pandas DataFrame remove Empty Cells](https://stackoverflow.com/questions/29314033/python-pandas-dataframe-remove-empty-cells) — user3483203, Aug 22 '18 at 05:43

score 4 · Accepted Answer · answered Aug 22 '18 at 05:34

You can use:

df = df.replace('', np.nan)

If want simplify your code is possible join regexes by | and for empty space use ^$:

df = pd.DataFrame({'A':list('abcdef'),
                   'B':['',5,4,5,5,4],
                   'C':['','  ','   ',4,2,3],
                   'D':[1,3,5,7,'       ',0],
                   'E':[5,3,6,9,2,4],
                   'F':list('aaabbb')})

df = df.replace(r'^\s+$|^\t+$|^$', np.nan, regex=True)
print (df)
   A    B    C    D  E  F
0  a  NaN  NaN  1.0  5  a
1  b  5.0  NaN  3.0  3  a
2  c  4.0  NaN  5.0  6  a
3  d  5.0  4.0  7.0  9  b
4  e  5.0  2.0  NaN  2  b
5  f  4.0  3.0  0.0  4  b

score 2 · Answer 2 · answered Aug 22 '18 at 05:35

Depending on your version of pandas you may do:

DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False) axis : {0 or ‘index’, 1 or ‘columns’}, default 0

Determine if rows or columns which contain missing values are removed.

0, or ‘index’ : Drop rows which contain missing values. 1, or ‘columns’ : Drop columns which contain missing value.
Deprecated since version 0.23.0:: Pass tuple or list to drop on multiple
axes. source

So, for now to drop rows with empty values

df = df.dropna(axis=0)

Should work

pandas: remove rows with missing data

2 Answers2

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