CODEWITHSUNDEEP
pythonpython-2.7pandasdataframe
Shubham Thagunna
Shubham Thagunna

Reputation: 127

delete all nan values from list in pandas dataframe

if any elements are there along with nan then i want to keep element and want to delete nan only like

example 1 ->

index      values
0     [nan,'a',nan,nan]

output should be like

index   values

0         [a]

example 2->

index      values
0     [nan,'a',b,c]

1     [nan,nan,nan]

output should be like

index   values

0      [a,b,c]

1        []

Upvotes: 7

Views: 6473

Answers (4)

jpp
jpp

Reputation: 164813

You can use the fact that np.nan == np.nan evaluates to False:

df = pd.DataFrame([[0, [np.nan, 'a', 'b', 'c']],
                   [1, [np.nan, np.nan, np.nan]],
                   [2, [np.nan, 'a', np.nan, np.nan]]],
                  columns=['index', 'values'])

df['values'] = df['values'].apply(lambda x: [i for i in x if i == i])

print(df)

   index     values
0      0  [a, b, c]
1      1         []
2      2        [a]

lambda is just an anonymous function. You could also use a named function:

def remove_nan(x):
    return [i for i in x if i == i]

df['values'] = df['values'].apply(remove_nan)

Related: Why is NaN not equal to NaN?

Upvotes: 5

Pyd
Pyd

Reputation: 6159

df['values'].apply(lambda v: pd.Series(v).dropna().values )

Upvotes: 2

konvas
konvas

Reputation: 14366

You can use pd.Series.map on df.values

import pandas as pd
my_filter = lambda x: not pd.isna(x)
df['new_values'] = df['values'].map(lambda x: list(filter(my_filter, x)))

Upvotes: 0

Rakesh
Rakesh

Reputation: 82785

This is one approach using df.apply.

import pandas as pd
import numpy as np

df = pd.DataFrame({"a": [[np.nan, np.nan, np.nan, "a", np.nan], [np.nan, np.nan], ["a", "b"]]})
df["a"] = df["a"].apply(lambda x: [i for i in x if str(i) != "nan"])
print(df)

Output:

        a
0     [a]
1      []
2  [a, b]

Upvotes: 8

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