Pandas drop columns based on column name AND content
I have a dataframe like this:
df = pd.DataFrame([[1,2,np.nan,np.nan,5],[3,4,np.nan,np.nan,6]],columns=['a','b','c','Unnamed: 4','Unnamed: 5'])
df
Out[16]:
a b c Unnamed: 4 Unnamed: 5
0 1 2 NaN NaN 5
1 3 4 NaN NaN 6
I want to drop columns that are BOTH all nan AND have 'Unnamed: ' in the name (as often happens when importing a dataframe from a file with columns that have no name in the header). Desired output:
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
I can do:
df[[col for col in df.columns if 'Unnamed: ' not in col]]
Out[18]:
a b c
0 1 2 NaN
1 3 4 NaN
or:
df.dropna(how='all',axis=1)
Out[19]:
a b Unnamed: 5
0 1 2 5
1 3 4 6
Is there a pythonic way to do both these things simultaneously (connected by AND not OR)?
python pandas dataframe
asked Nov 14 '18 at 23:09
andbeonetravelerandbeonetraveler
178213
add a comment |
I have a dataframe like this:
df = pd.DataFrame([[1,2,np.nan,np.nan,5],[3,4,np.nan,np.nan,6]],columns=['a','b','c','Unnamed: 4','Unnamed: 5'])
df
Out[16]:
a b c Unnamed: 4 Unnamed: 5
0 1 2 NaN NaN 5
1 3 4 NaN NaN 6
I want to drop columns that are BOTH all nan AND have 'Unnamed: ' in the name (as often happens when importing a dataframe from a file with columns that have no name in the header). Desired output:
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
I can do:
df[[col for col in df.columns if 'Unnamed: ' not in col]]
Out[18]:
a b c
0 1 2 NaN
1 3 4 NaN
or:
df.dropna(how='all',axis=1)
Out[19]:
a b Unnamed: 5
0 1 2 5
1 3 4 6
Is there a pythonic way to do both these things simultaneously (connected by AND not OR)?
python pandas dataframe
asked Nov 14 '18 at 23:09
andbeonetravelerandbeonetraveler
178213
add a comment |
I have a dataframe like this:
df = pd.DataFrame([[1,2,np.nan,np.nan,5],[3,4,np.nan,np.nan,6]],columns=['a','b','c','Unnamed: 4','Unnamed: 5'])
df
Out[16]:
a b c Unnamed: 4 Unnamed: 5
0 1 2 NaN NaN 5
1 3 4 NaN NaN 6
I want to drop columns that are BOTH all nan AND have 'Unnamed: ' in the name (as often happens when importing a dataframe from a file with columns that have no name in the header). Desired output:
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
I can do:
df[[col for col in df.columns if 'Unnamed: ' not in col]]
Out[18]:
a b c
0 1 2 NaN
1 3 4 NaN
or:
df.dropna(how='all',axis=1)
Out[19]:
a b Unnamed: 5
0 1 2 5
1 3 4 6
Is there a pythonic way to do both these things simultaneously (connected by AND not OR)?
python pandas dataframe
asked Nov 14 '18 at 23:09
andbeonetravelerandbeonetraveler
178213
I have a dataframe like this:
df = pd.DataFrame([[1,2,np.nan,np.nan,5],[3,4,np.nan,np.nan,6]],columns=['a','b','c','Unnamed: 4','Unnamed: 5'])
df
Out[16]:
a b c Unnamed: 4 Unnamed: 5
0 1 2 NaN NaN 5
1 3 4 NaN NaN 6
I want to drop columns that are BOTH all nan AND have 'Unnamed: ' in the name (as often happens when importing a dataframe from a file with columns that have no name in the header). Desired output:
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
I can do:
df[[col for col in df.columns if 'Unnamed: ' not in col]]
Out[18]:
a b c
0 1 2 NaN
1 3 4 NaN
or:
df.dropna(how='all',axis=1)
Out[19]:
a b Unnamed: 5
0 1 2 5
1 3 4 6
Is there a pythonic way to do both these things simultaneously (connected by AND not OR)?
python pandas dataframe
python pandas dataframe
asked Nov 14 '18 at 23:09
andbeonetravelerandbeonetraveler
178213
asked Nov 14 '18 at 23:09
andbeonetravelerandbeonetraveler
178213
asked Nov 14 '18 at 23:09
andbeonetravelerandbeonetraveler
178213
asked Nov 14 '18 at 23:09
andbeonetravelerandbeonetraveler
178213
asked Nov 14 '18 at 23:09
andbeonetravelerandbeonetraveler
178213
178213
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
filter + isnull + drop
First filter your dataframe for column labels, then calculate which are all null:
nulls = df.filter(like='Unnamed').isnull().all()
df = df.drop(nulls[nulls].index, axis='columns')
print(df)
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
answered Nov 14 '18 at 23:13
jppjpp
101k2163112
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
filter + isnull + drop
First filter your dataframe for column labels, then calculate which are all null:
nulls = df.filter(like='Unnamed').isnull().all()
df = df.drop(nulls[nulls].index, axis='columns')
print(df)
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
answered Nov 14 '18 at 23:13
jppjpp
101k2163112
add a comment |
filter + isnull + drop
First filter your dataframe for column labels, then calculate which are all null:
nulls = df.filter(like='Unnamed').isnull().all()
df = df.drop(nulls[nulls].index, axis='columns')
print(df)
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
answered Nov 14 '18 at 23:13
jppjpp
101k2163112
add a comment |
filter + isnull + drop
First filter your dataframe for column labels, then calculate which are all null:
nulls = df.filter(like='Unnamed').isnull().all()
df = df.drop(nulls[nulls].index, axis='columns')
print(df)
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
answered Nov 14 '18 at 23:13
jppjpp
101k2163112
filter + isnull + drop
First filter your dataframe for column labels, then calculate which are all null:
nulls = df.filter(like='Unnamed').isnull().all()
df = df.drop(nulls[nulls].index, axis='columns')
print(df)
a b c Unnamed: 5
0 1 2 NaN 5
1 3 4 NaN 6
answered Nov 14 '18 at 23:13
jppjpp
101k2163112
answered Nov 14 '18 at 23:13
jppjpp
101k2163112
answered Nov 14 '18 at 23:13
jppjpp
101k2163112
answered Nov 14 '18 at 23:13
jppjpp
101k2163112
101k2163112
add a comment |
add a comment |
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