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How do i drop all columns that include '_id' - Python
How can I safely create a nested directory in Python?How to get the current time in PythonHow can I make a time delay in Python?How do I list all files of a directory?Find all files in a directory with extension .txt in PythonDrop data frame columns by nameRenaming columns in pandasAdding new column to existing DataFrame in Python pandasSelect rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headers
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I have a dataframe with 247 columns. Many of the column names contain "_id" in the column name. How do I drop all columns that contain "_id"??
python dataframe data-cleaning
asked Mar 23 at 1:04
GuyGuyGuyGuyGuyGuy
104
add a comment |
I have a dataframe with 247 columns. Many of the column names contain "_id" in the column name. How do I drop all columns that contain "_id"??
python dataframe data-cleaning
asked Mar 23 at 1:04
GuyGuyGuyGuyGuyGuy
104
add a comment |
I have a dataframe with 247 columns. Many of the column names contain "_id" in the column name. How do I drop all columns that contain "_id"??
python dataframe data-cleaning
asked Mar 23 at 1:04
GuyGuyGuyGuyGuyGuy
104
I have a dataframe with 247 columns. Many of the column names contain "_id" in the column name. How do I drop all columns that contain "_id"??
python dataframe data-cleaning
python dataframe data-cleaning
asked Mar 23 at 1:04
GuyGuyGuyGuyGuyGuy
104
asked Mar 23 at 1:04
GuyGuyGuyGuyGuyGuy
104
asked Mar 23 at 1:04
GuyGuyGuyGuyGuyGuy
104
asked Mar 23 at 1:04
GuyGuyGuyGuyGuyGuy
104
asked Mar 23 at 1:04
GuyGuyGuyGuyGuyGuy
104
104
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.
df = df.loc[:, ~df.columns.str.contains("_id")]
answered Mar 23 at 7:11
run-outrun-out
1,2571214
add a comment |
Try this:
df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]
What this code does is:
To filter all those columns which will have _id anywhere in its name and then dropping all those columns.
let me know if you didn't understand or need any help in this regard.
answered Mar 23 at 1:34
Faizan KhanFaizan Khan
437312
let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks
– Faizan Khan
Mar 23 at 1:35
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
This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.
df = df.loc[:, ~df.columns.str.contains("_id")]
answered Mar 23 at 7:11
run-outrun-out
1,2571214
add a comment |
This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.
df = df.loc[:, ~df.columns.str.contains("_id")]
answered Mar 23 at 7:11
run-outrun-out
1,2571214
add a comment |
This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.
df = df.loc[:, ~df.columns.str.contains("_id")]
answered Mar 23 at 7:11
run-outrun-out
1,2571214
This is pretty straight forward as well. Select the columns that contain "_id" and then invert it, use .loc to restrict the columns, and you're done.
df = df.loc[:, ~df.columns.str.contains("_id")]
answered Mar 23 at 7:11
run-outrun-out
1,2571214
answered Mar 23 at 7:11
run-outrun-out
1,2571214
answered Mar 23 at 7:11
run-outrun-out
1,2571214
answered Mar 23 at 7:11
run-outrun-out
1,2571214
1,2571214
add a comment |
add a comment |
Try this:
df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]
What this code does is:
To filter all those columns which will have _id anywhere in its name and then dropping all those columns.
let me know if you didn't understand or need any help in this regard.
answered Mar 23 at 1:34
Faizan KhanFaizan Khan
437312
let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks
– Faizan Khan
Mar 23 at 1:35
add a comment |
Try this:
df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]
What this code does is:
To filter all those columns which will have _id anywhere in its name and then dropping all those columns.
let me know if you didn't understand or need any help in this regard.
answered Mar 23 at 1:34
Faizan KhanFaizan Khan
437312
let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks
– Faizan Khan
Mar 23 at 1:35
add a comment |
Try this:
df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]
What this code does is:
To filter all those columns which will have _id anywhere in its name and then dropping all those columns.
let me know if you didn't understand or need any help in this regard.
answered Mar 23 at 1:34
Faizan KhanFaizan Khan
437312
Try this:
df = df[df.columns.drop(list(df.filter(like='_id')), axis = 1, inplace = True)]
What this code does is:
To filter all those columns which will have _id anywhere in its name and then dropping all those columns.
let me know if you didn't understand or need any help in this regard.
answered Mar 23 at 1:34
Faizan KhanFaizan Khan
437312
answered Mar 23 at 1:34
Faizan KhanFaizan Khan
437312
answered Mar 23 at 1:34
Faizan KhanFaizan Khan
437312
answered Mar 23 at 1:34
Faizan KhanFaizan Khan
437312
437312
let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks
– Faizan Khan
Mar 23 at 1:35
add a comment |
let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks
– Faizan Khan
Mar 23 at 1:35
let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks
– Faizan Khan
Mar 23 at 1:35
let me know if you need any other help in this regard. And if you find the answer useful, please consider accepting the answer by clicking on the tick mark(grey colored) on the left of the answer. And also upvote the answer. Thanks
– Faizan Khan
Mar 23 at 1:35
add a comment |
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