Pandas: Split a Dataframe into separate Dataframes based on certain Column's string values
Haven't found any answers that I could apply to my problem so here it goes:
I have an initial dataframe of images that I would like to split into two, based on the description of that image, which is a string in the "Description" column.
My problem issue is that not all descriptions are equally written. Here's an example of what I mean:
Some images are accelerated and others aren't. That's the criteria I want to use to split the dataset.
However even accelerated and non-accelerated image descriptions vary among them.
My strategy would be to rename every string that has "ACC" in it - this would cover all accelerated images - to "ACCELERATED IMAGE".
Then I could do:
df_Accl = df[df.Description == "ACCELERATED IMAGE"]
df_NonAccl = df[df.Description != "ACCELERATED IMAGE"]
How can I achieve this? This was just a strategy that I came up with, if there's any other more efficient way of doing this feel free to speak it.
python string pandas dataframe
asked Nov 18 '18 at 17:49
J. DevezJ. Devez
599
add a comment |
Haven't found any answers that I could apply to my problem so here it goes:
I have an initial dataframe of images that I would like to split into two, based on the description of that image, which is a string in the "Description" column.
My problem issue is that not all descriptions are equally written. Here's an example of what I mean:
Some images are accelerated and others aren't. That's the criteria I want to use to split the dataset.
However even accelerated and non-accelerated image descriptions vary among them.
My strategy would be to rename every string that has "ACC" in it - this would cover all accelerated images - to "ACCELERATED IMAGE".
Then I could do:
df_Accl = df[df.Description == "ACCELERATED IMAGE"]
df_NonAccl = df[df.Description != "ACCELERATED IMAGE"]
How can I achieve this? This was just a strategy that I came up with, if there's any other more efficient way of doing this feel free to speak it.
python string pandas dataframe
asked Nov 18 '18 at 17:49
J. DevezJ. Devez
599
1
Please try to avoid images and put some data that can be easily loaded next time..
– Franco Piccolo
Nov 18 '18 at 17:58
Related: Splitting a dataframe based on condition
– jpp
Nov 18 '18 at 18:07
add a comment |
Haven't found any answers that I could apply to my problem so here it goes:
I have an initial dataframe of images that I would like to split into two, based on the description of that image, which is a string in the "Description" column.
My problem issue is that not all descriptions are equally written. Here's an example of what I mean:
Some images are accelerated and others aren't. That's the criteria I want to use to split the dataset.
However even accelerated and non-accelerated image descriptions vary among them.
My strategy would be to rename every string that has "ACC" in it - this would cover all accelerated images - to "ACCELERATED IMAGE".
Then I could do:
df_Accl = df[df.Description == "ACCELERATED IMAGE"]
df_NonAccl = df[df.Description != "ACCELERATED IMAGE"]
How can I achieve this? This was just a strategy that I came up with, if there's any other more efficient way of doing this feel free to speak it.
python string pandas dataframe
asked Nov 18 '18 at 17:49
J. DevezJ. Devez
599
Haven't found any answers that I could apply to my problem so here it goes:
I have an initial dataframe of images that I would like to split into two, based on the description of that image, which is a string in the "Description" column.
My problem issue is that not all descriptions are equally written. Here's an example of what I mean:
Some images are accelerated and others aren't. That's the criteria I want to use to split the dataset.
However even accelerated and non-accelerated image descriptions vary among them.
My strategy would be to rename every string that has "ACC" in it - this would cover all accelerated images - to "ACCELERATED IMAGE".
Then I could do:
df_Accl = df[df.Description == "ACCELERATED IMAGE"]
df_NonAccl = df[df.Description != "ACCELERATED IMAGE"]
How can I achieve this? This was just a strategy that I came up with, if there's any other more efficient way of doing this feel free to speak it.
python string pandas dataframe
python string pandas dataframe
asked Nov 18 '18 at 17:49
J. DevezJ. Devez
599
asked Nov 18 '18 at 17:49
J. DevezJ. Devez
599
asked Nov 18 '18 at 17:49
J. DevezJ. Devez
599
asked Nov 18 '18 at 17:49
J. DevezJ. Devez
599
asked Nov 18 '18 at 17:49
J. DevezJ. Devez
599
599
1
Please try to avoid images and put some data that can be easily loaded next time..
– Franco Piccolo
Nov 18 '18 at 17:58
Related: Splitting a dataframe based on condition
– jpp
Nov 18 '18 at 18:07
add a comment |
1
Please try to avoid images and put some data that can be easily loaded next time..
– Franco Piccolo
Nov 18 '18 at 17:58
Related: Splitting a dataframe based on condition
– jpp
Nov 18 '18 at 18:07
1
1
Please try to avoid images and put some data that can be easily loaded next time..
– Franco Piccolo
Nov 18 '18 at 17:58
Please try to avoid images and put some data that can be easily loaded next time..
– Franco Piccolo
Nov 18 '18 at 17:58
Related: Splitting a dataframe based on condition
– jpp
Nov 18 '18 at 18:07
Related: Splitting a dataframe based on condition
– jpp
Nov 18 '18 at 18:07
add a comment |
2 Answers
2
active
oldest
votes
You can use str.contains for boolean mask - then filter by boolean indexing.
For invert mask is use ~, filter rows not contains ACC:
mask = df.Description.str.contains("ACC")
df_Accl = df[mask]
df_NonAccl = df[~mask]
answered Nov 18 '18 at 17:56
jezraeljezrael
333k24276352
@KhalilAlHooti - thank you.
– jezrael
Nov 18 '18 at 18:00
1
This works (plus it's much more efficient than the strategy I was thinking)!
– J. Devez
Nov 18 '18 at 18:21
add a comment |
You can use contains to find the rows that contain the substring ACC:
df['Description'].str.contains('ACC')
answered Nov 18 '18 at 17:56
Franco PiccoloFranco Piccolo
1,576712
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
You can use str.contains for boolean mask - then filter by boolean indexing.
For invert mask is use ~, filter rows not contains ACC:
mask = df.Description.str.contains("ACC")
df_Accl = df[mask]
df_NonAccl = df[~mask]
answered Nov 18 '18 at 17:56
jezraeljezrael
333k24276352
@KhalilAlHooti - thank you.
– jezrael
Nov 18 '18 at 18:00
1
This works (plus it's much more efficient than the strategy I was thinking)!
– J. Devez
Nov 18 '18 at 18:21
add a comment |
You can use str.contains for boolean mask - then filter by boolean indexing.
For invert mask is use ~, filter rows not contains ACC:
mask = df.Description.str.contains("ACC")
df_Accl = df[mask]
df_NonAccl = df[~mask]
answered Nov 18 '18 at 17:56
jezraeljezrael
333k24276352
@KhalilAlHooti - thank you.
– jezrael
Nov 18 '18 at 18:00
1
This works (plus it's much more efficient than the strategy I was thinking)!
– J. Devez
Nov 18 '18 at 18:21
add a comment |
You can use str.contains for boolean mask - then filter by boolean indexing.
For invert mask is use ~, filter rows not contains ACC:
mask = df.Description.str.contains("ACC")
df_Accl = df[mask]
df_NonAccl = df[~mask]
answered Nov 18 '18 at 17:56
jezraeljezrael
333k24276352
You can use str.contains for boolean mask - then filter by boolean indexing.
For invert mask is use ~, filter rows not contains ACC:
mask = df.Description.str.contains("ACC")
df_Accl = df[mask]
df_NonAccl = df[~mask]
answered Nov 18 '18 at 17:56
jezraeljezrael
333k24276352
answered Nov 18 '18 at 17:56
jezraeljezrael
333k24276352
answered Nov 18 '18 at 17:56
jezraeljezrael
333k24276352
answered Nov 18 '18 at 17:56
jezraeljezrael
333k24276352
333k24276352
@KhalilAlHooti - thank you.
– jezrael
Nov 18 '18 at 18:00
1
This works (plus it's much more efficient than the strategy I was thinking)!
– J. Devez
Nov 18 '18 at 18:21
add a comment |
@KhalilAlHooti - thank you.
– jezrael
Nov 18 '18 at 18:00
1
This works (plus it's much more efficient than the strategy I was thinking)!
– J. Devez
Nov 18 '18 at 18:21
@KhalilAlHooti - thank you.
– jezrael
Nov 18 '18 at 18:00
@KhalilAlHooti - thank you.
– jezrael
Nov 18 '18 at 18:00
1
1
This works (plus it's much more efficient than the strategy I was thinking)!
– J. Devez
Nov 18 '18 at 18:21
This works (plus it's much more efficient than the strategy I was thinking)!
– J. Devez
Nov 18 '18 at 18:21
add a comment |
You can use contains to find the rows that contain the substring ACC:
df['Description'].str.contains('ACC')
answered Nov 18 '18 at 17:56
Franco PiccoloFranco Piccolo
1,576712
add a comment |
You can use contains to find the rows that contain the substring ACC:
df['Description'].str.contains('ACC')
answered Nov 18 '18 at 17:56
Franco PiccoloFranco Piccolo
1,576712
add a comment |
You can use contains to find the rows that contain the substring ACC:
df['Description'].str.contains('ACC')
answered Nov 18 '18 at 17:56
Franco PiccoloFranco Piccolo
1,576712
You can use contains to find the rows that contain the substring ACC:
df['Description'].str.contains('ACC')
answered Nov 18 '18 at 17:56
Franco PiccoloFranco Piccolo
1,576712
answered Nov 18 '18 at 17:56
Franco PiccoloFranco Piccolo
1,576712
answered Nov 18 '18 at 17:56
Franco PiccoloFranco Piccolo
1,576712
answered Nov 18 '18 at 17:56
Franco PiccoloFranco Piccolo
1,576712
1,576712
add a comment |
add a comment |
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1
Please try to avoid images and put some data that can be easily loaded next time..
– Franco Piccolo
Nov 18 '18 at 17:58
Related: Splitting a dataframe based on condition
– jpp
Nov 18 '18 at 18:07