how to extract a column in a for loop out of a dataframe from multiple list glob created by glob
I am a beginner in Python and just started with machine learning. I can not figure out how to extract a column out of df_test.
I would like to extract column 280 (this is the target y for a linearregression, y_test)
I used glob, to combine multiple cvs files, which are the test set for the regression. I have to extract x_test which is columns 0-279 and all rows. and y_test column 280
from glob import glob
filenames= glob('webStats_test*.csv')
df_test= [pd.read_csv(f)for f in filenames]
new_df = pd.DataFrame()
really hope you could help, already spend ours looking and trying but with no results.
Kind regards irmgard
python-3.x pandas glob
edited Nov 10 at 19:11
desertnaut
16.1k63466
asked Nov 10 at 17:09
Irm
91
add a comment |
I am a beginner in Python and just started with machine learning. I can not figure out how to extract a column out of df_test.
I would like to extract column 280 (this is the target y for a linearregression, y_test)
I used glob, to combine multiple cvs files, which are the test set for the regression. I have to extract x_test which is columns 0-279 and all rows. and y_test column 280
from glob import glob
filenames= glob('webStats_test*.csv')
df_test= [pd.read_csv(f)for f in filenames]
new_df = pd.DataFrame()
really hope you could help, already spend ours looking and trying but with no results.
Kind regards irmgard
python-3.x pandas glob
edited Nov 10 at 19:11
desertnaut
16.1k63466
asked Nov 10 at 17:09
Irm
91
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Nov 10 at 19:11
add a comment |
I am a beginner in Python and just started with machine learning. I can not figure out how to extract a column out of df_test.
I would like to extract column 280 (this is the target y for a linearregression, y_test)
I used glob, to combine multiple cvs files, which are the test set for the regression. I have to extract x_test which is columns 0-279 and all rows. and y_test column 280
from glob import glob
filenames= glob('webStats_test*.csv')
df_test= [pd.read_csv(f)for f in filenames]
new_df = pd.DataFrame()
really hope you could help, already spend ours looking and trying but with no results.
Kind regards irmgard
python-3.x pandas glob
edited Nov 10 at 19:11
desertnaut
16.1k63466
asked Nov 10 at 17:09
Irm
91
I am a beginner in Python and just started with machine learning. I can not figure out how to extract a column out of df_test.
I would like to extract column 280 (this is the target y for a linearregression, y_test)
I used glob, to combine multiple cvs files, which are the test set for the regression. I have to extract x_test which is columns 0-279 and all rows. and y_test column 280
from glob import glob
filenames= glob('webStats_test*.csv')
df_test= [pd.read_csv(f)for f in filenames]
new_df = pd.DataFrame()
really hope you could help, already spend ours looking and trying but with no results.
Kind regards irmgard
python-3.x pandas glob
python-3.x pandas glob
edited Nov 10 at 19:11
desertnaut
16.1k63466
asked Nov 10 at 17:09
Irm
91
edited Nov 10 at 19:11
desertnaut
16.1k63466
asked Nov 10 at 17:09
Irm
91
edited Nov 10 at 19:11
desertnaut
16.1k63466
edited Nov 10 at 19:11
desertnaut
16.1k63466
edited Nov 10 at 19:11
desertnaut
16.1k63466
16.1k63466
asked Nov 10 at 17:09
Irm
91
asked Nov 10 at 17:09
Irm
91
asked Nov 10 at 17:09
Irm
91
91
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Nov 10 at 19:11
add a comment |
Question has nothing to do withmachine-learning- kindly do not spam the tag (removed).
– desertnaut
Nov 10 at 19:11
Question has nothing to do with
machine-learning - kindly do not spam the tag (removed).– desertnaut
Nov 10 at 19:11
Question has nothing to do with
machine-learning - kindly do not spam the tag (removed).– desertnaut
Nov 10 at 19:11
add a comment |
1 Answer
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You can use pandas .iloc for this.
x_test = new_df.iloc[:, :280]
y_test = new_df.iloc[:, 280]
But in your code, you still first have to fill new_df with actual data... I would append all your different files to a list of DataFrames and then use concat to combine the list of DataFrames to 1 DataFrame.
answered Nov 10 at 20:18
Niels Henkens
290112
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
You can use pandas .iloc for this.
x_test = new_df.iloc[:, :280]
y_test = new_df.iloc[:, 280]
But in your code, you still first have to fill new_df with actual data... I would append all your different files to a list of DataFrames and then use concat to combine the list of DataFrames to 1 DataFrame.
answered Nov 10 at 20:18
Niels Henkens
290112
add a comment |
You can use pandas .iloc for this.
x_test = new_df.iloc[:, :280]
y_test = new_df.iloc[:, 280]
But in your code, you still first have to fill new_df with actual data... I would append all your different files to a list of DataFrames and then use concat to combine the list of DataFrames to 1 DataFrame.
answered Nov 10 at 20:18
Niels Henkens
290112
add a comment |
You can use pandas .iloc for this.
x_test = new_df.iloc[:, :280]
y_test = new_df.iloc[:, 280]
But in your code, you still first have to fill new_df with actual data... I would append all your different files to a list of DataFrames and then use concat to combine the list of DataFrames to 1 DataFrame.
answered Nov 10 at 20:18
Niels Henkens
290112
You can use pandas .iloc for this.
x_test = new_df.iloc[:, :280]
y_test = new_df.iloc[:, 280]
But in your code, you still first have to fill new_df with actual data... I would append all your different files to a list of DataFrames and then use concat to combine the list of DataFrames to 1 DataFrame.
answered Nov 10 at 20:18
Niels Henkens
290112
answered Nov 10 at 20:18
Niels Henkens
290112
answered Nov 10 at 20:18
Niels Henkens
290112
answered Nov 10 at 20:18
Niels Henkens
290112
290112
add a comment |
add a comment |
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Question has nothing to do with
machine-learning- kindly do not spam the tag (removed).– desertnaut
Nov 10 at 19:11