How to filter a transposed pandas dataframe?
0
Say I have a transposed df like so
id 0 1 2 3
0 1361 Spain Russia South Africa China
1 1741 Portugal Cuba UK Ukraine
2 1783 Germany USA France Egypt
3 1353 Brazil Russia Japan Kenya
4 1458 India Romania Holland Nigeria
How could I get all rows where there is 'er' so it'll return me this
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
because 'er' is contained in Germany and Nigeria.
Thanks!
python pandas
asked Nov 15 '18 at 18:24
Del-m10Del-m10
11
add a comment |
0
Say I have a transposed df like so
id 0 1 2 3
0 1361 Spain Russia South Africa China
1 1741 Portugal Cuba UK Ukraine
2 1783 Germany USA France Egypt
3 1353 Brazil Russia Japan Kenya
4 1458 India Romania Holland Nigeria
How could I get all rows where there is 'er' so it'll return me this
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
because 'er' is contained in Germany and Nigeria.
Thanks!
python pandas
asked Nov 15 '18 at 18:24
Del-m10Del-m10
11
add a comment |
0
0
0
Say I have a transposed df like so
id 0 1 2 3
0 1361 Spain Russia South Africa China
1 1741 Portugal Cuba UK Ukraine
2 1783 Germany USA France Egypt
3 1353 Brazil Russia Japan Kenya
4 1458 India Romania Holland Nigeria
How could I get all rows where there is 'er' so it'll return me this
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
because 'er' is contained in Germany and Nigeria.
Thanks!
python pandas
asked Nov 15 '18 at 18:24
Del-m10Del-m10
11
Say I have a transposed df like so
id 0 1 2 3
0 1361 Spain Russia South Africa China
1 1741 Portugal Cuba UK Ukraine
2 1783 Germany USA France Egypt
3 1353 Brazil Russia Japan Kenya
4 1458 India Romania Holland Nigeria
How could I get all rows where there is 'er' so it'll return me this
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
because 'er' is contained in Germany and Nigeria.
Thanks!
python pandas
python pandas
asked Nov 15 '18 at 18:24
Del-m10Del-m10
11
asked Nov 15 '18 at 18:24
Del-m10Del-m10
11
asked Nov 15 '18 at 18:24
Del-m10Del-m10
11
asked Nov 15 '18 at 18:24
Del-m10Del-m10
11
asked Nov 15 '18 at 18:24
Del-m10Del-m10
11
11
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
1
Using contains
df[df.apply(lambda x :x.str.contains(pat='er')).any(1)]
Out[96]:
id 0 1 2 3
2 1783 Germany USA France Egypt None
4 1458 India Romania Holland Nigeria None
answered Nov 15 '18 at 18:27
Wen-BenWen-Ben
120k83569
Thank you very much!
– Del-m10
Nov 16 '18 at 9:51
add a comment |
0
Use apply + str.contains across rows:
df = df[df.apply(lambda x: x.str.contains('er').any(), axis=1)]
print(df)
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
answered Nov 15 '18 at 18:27
Sandeep KadapaSandeep Kadapa
7,398831
1
also useful too cheers mate!
– Del-m10
Nov 16 '18 at 10:49
@Del-m10 Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:50
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
Using contains
df[df.apply(lambda x :x.str.contains(pat='er')).any(1)]
Out[96]:
id 0 1 2 3
2 1783 Germany USA France Egypt None
4 1458 India Romania Holland Nigeria None
answered Nov 15 '18 at 18:27
Wen-BenWen-Ben
120k83569
Thank you very much!
– Del-m10
Nov 16 '18 at 9:51
add a comment |
1
Using contains
df[df.apply(lambda x :x.str.contains(pat='er')).any(1)]
Out[96]:
id 0 1 2 3
2 1783 Germany USA France Egypt None
4 1458 India Romania Holland Nigeria None
answered Nov 15 '18 at 18:27
Wen-BenWen-Ben
120k83569
Thank you very much!
– Del-m10
Nov 16 '18 at 9:51
add a comment |
1
1
1
Using contains
df[df.apply(lambda x :x.str.contains(pat='er')).any(1)]
Out[96]:
id 0 1 2 3
2 1783 Germany USA France Egypt None
4 1458 India Romania Holland Nigeria None
answered Nov 15 '18 at 18:27
Wen-BenWen-Ben
120k83569
Using contains
df[df.apply(lambda x :x.str.contains(pat='er')).any(1)]
Out[96]:
id 0 1 2 3
2 1783 Germany USA France Egypt None
4 1458 India Romania Holland Nigeria None
answered Nov 15 '18 at 18:27
Wen-BenWen-Ben
120k83569
answered Nov 15 '18 at 18:27
Wen-BenWen-Ben
120k83569
answered Nov 15 '18 at 18:27
Wen-BenWen-Ben
120k83569
answered Nov 15 '18 at 18:27
Wen-BenWen-Ben
120k83569
120k83569
Thank you very much!
– Del-m10
Nov 16 '18 at 9:51
add a comment |
Thank you very much!
– Del-m10
Nov 16 '18 at 9:51
Thank you very much!
– Del-m10
Nov 16 '18 at 9:51
Thank you very much!
– Del-m10
Nov 16 '18 at 9:51
add a comment |
0
Use apply + str.contains across rows:
df = df[df.apply(lambda x: x.str.contains('er').any(), axis=1)]
print(df)
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
answered Nov 15 '18 at 18:27
Sandeep KadapaSandeep Kadapa
7,398831
1
also useful too cheers mate!
– Del-m10
Nov 16 '18 at 10:49
@Del-m10 Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:50
add a comment |
0
Use apply + str.contains across rows:
df = df[df.apply(lambda x: x.str.contains('er').any(), axis=1)]
print(df)
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
answered Nov 15 '18 at 18:27
Sandeep KadapaSandeep Kadapa
7,398831
1
also useful too cheers mate!
– Del-m10
Nov 16 '18 at 10:49
@Del-m10 Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:50
add a comment |
0
0
0
Use apply + str.contains across rows:
df = df[df.apply(lambda x: x.str.contains('er').any(), axis=1)]
print(df)
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
answered Nov 15 '18 at 18:27
Sandeep KadapaSandeep Kadapa
7,398831
Use apply + str.contains across rows:
df = df[df.apply(lambda x: x.str.contains('er').any(), axis=1)]
print(df)
id 0 1 2 3
2 1783 Germany USA France Egypt
4 1458 India Romania Holland Nigeria
answered Nov 15 '18 at 18:27
Sandeep KadapaSandeep Kadapa
7,398831
answered Nov 15 '18 at 18:27
Sandeep KadapaSandeep Kadapa
7,398831
answered Nov 15 '18 at 18:27
Sandeep KadapaSandeep Kadapa
7,398831
answered Nov 15 '18 at 18:27
Sandeep KadapaSandeep Kadapa
7,398831
7,398831
1
also useful too cheers mate!
– Del-m10
Nov 16 '18 at 10:49
@Del-m10 Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:50
add a comment |
1
also useful too cheers mate!
– Del-m10
Nov 16 '18 at 10:49
@Del-m10 Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:50
1
1
also useful too cheers mate!
– Del-m10
Nov 16 '18 at 10:49
also useful too cheers mate!
– Del-m10
Nov 16 '18 at 10:49
@Del-m10 Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:50
@Del-m10 Glad to help.
– Sandeep Kadapa
Nov 16 '18 at 10:50
add a comment |
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