How to reduce columns in dataframe pandas
7
Here how the datalooks like in df dataframe:
A B C D
0.js 2 1 1 -1
1.js 3 -5 1 -4
total 5 -4 2 -5
And I would get new dataframe df1:
A C
0.js 2 1
1.js 3 1
total 5 2
So basically it should look like this:
df1 = df[df["total"] > 0]
but it should filter on row instead of column and I can't figure it out..
python pandas dataframe
edited Feb 5 at 20:50
Scott Boston
56.1k73157
asked Feb 5 at 20:24
J OderbergJ Oderberg
404
add a comment |
7
Here how the datalooks like in df dataframe:
A B C D
0.js 2 1 1 -1
1.js 3 -5 1 -4
total 5 -4 2 -5
And I would get new dataframe df1:
A C
0.js 2 1
1.js 3 1
total 5 2
So basically it should look like this:
df1 = df[df["total"] > 0]
but it should filter on row instead of column and I can't figure it out..
python pandas dataframe
edited Feb 5 at 20:50
Scott Boston
56.1k73157
asked Feb 5 at 20:24
J OderbergJ Oderberg
404
2
Do you want to only keep the columns where the condition is met ALWAYS, even if the condition is not met once in another column.
– Edeki Okoh
Feb 5 at 20:26
2
Can't you transpose and do the filter?
– Andrew Naguib
Feb 5 at 20:27
YourDataFrameis just a single row?
– ALollz
Feb 5 at 20:30
not a single row
– J Oderberg
Feb 5 at 20:32
Please, add a reproducible dataframe :)
– Andrew Naguib
Feb 5 at 20:39
add a comment |
7
7
7
1
Here how the datalooks like in df dataframe:
A B C D
0.js 2 1 1 -1
1.js 3 -5 1 -4
total 5 -4 2 -5
And I would get new dataframe df1:
A C
0.js 2 1
1.js 3 1
total 5 2
So basically it should look like this:
df1 = df[df["total"] > 0]
but it should filter on row instead of column and I can't figure it out..
python pandas dataframe
edited Feb 5 at 20:50
Scott Boston
56.1k73157
asked Feb 5 at 20:24
J OderbergJ Oderberg
404
Here how the datalooks like in df dataframe:
A B C D
0.js 2 1 1 -1
1.js 3 -5 1 -4
total 5 -4 2 -5
And I would get new dataframe df1:
A C
0.js 2 1
1.js 3 1
total 5 2
So basically it should look like this:
df1 = df[df["total"] > 0]
but it should filter on row instead of column and I can't figure it out..
python pandas dataframe
python pandas dataframe
edited Feb 5 at 20:50
Scott Boston
56.1k73157
asked Feb 5 at 20:24
J OderbergJ Oderberg
404
edited Feb 5 at 20:50
Scott Boston
56.1k73157
asked Feb 5 at 20:24
J OderbergJ Oderberg
404
edited Feb 5 at 20:50
Scott Boston
56.1k73157
edited Feb 5 at 20:50
Scott Boston
56.1k73157
edited Feb 5 at 20:50
Scott Boston
56.1k73157
56.1k73157
asked Feb 5 at 20:24
J OderbergJ Oderberg
404
asked Feb 5 at 20:24
J OderbergJ Oderberg
404
asked Feb 5 at 20:24
J OderbergJ Oderberg
404
404
2
Do you want to only keep the columns where the condition is met ALWAYS, even if the condition is not met once in another column.
– Edeki Okoh
Feb 5 at 20:26
2
Can't you transpose and do the filter?
– Andrew Naguib
Feb 5 at 20:27
YourDataFrameis just a single row?
– ALollz
Feb 5 at 20:30
not a single row
– J Oderberg
Feb 5 at 20:32
Please, add a reproducible dataframe :)
– Andrew Naguib
Feb 5 at 20:39
add a comment |
2
Do you want to only keep the columns where the condition is met ALWAYS, even if the condition is not met once in another column.
– Edeki Okoh
Feb 5 at 20:26
2
Can't you transpose and do the filter?
– Andrew Naguib
Feb 5 at 20:27
YourDataFrameis just a single row?
– ALollz
Feb 5 at 20:30
not a single row
– J Oderberg
Feb 5 at 20:32
Please, add a reproducible dataframe :)
– Andrew Naguib
Feb 5 at 20:39
2
2
Do you want to only keep the columns where the condition is met ALWAYS, even if the condition is not met once in another column.
– Edeki Okoh
Feb 5 at 20:26
Do you want to only keep the columns where the condition is met ALWAYS, even if the condition is not met once in another column.
– Edeki Okoh
Feb 5 at 20:26
2
2
Can't you transpose and do the filter?
– Andrew Naguib
Feb 5 at 20:27
Can't you transpose and do the filter?
– Andrew Naguib
Feb 5 at 20:27
Your
DataFrame is just a single row?– ALollz
Feb 5 at 20:30
Your
DataFrame is just a single row?– ALollz
Feb 5 at 20:30
not a single row
– J Oderberg
Feb 5 at 20:32
not a single row
– J Oderberg
Feb 5 at 20:32
Please, add a reproducible dataframe :)
– Andrew Naguib
Feb 5 at 20:39
Please, add a reproducible dataframe :)
– Andrew Naguib
Feb 5 at 20:39
add a comment |
3 Answers
3
active
oldest
votes
2
You can use, loc with boolean indexing or reindex:
df.loc[:, df.columns[(df.loc['total'] > 0)]]
OR
df.reindex(df.columns[(df.loc['total'] > 0)], axis=1)
Output:
A C
0.js 2 1
1.js 3 1
total 5 2
answered Feb 5 at 20:48
Scott BostonScott Boston
56.1k73157
add a comment |
4
You want to use .loc[:, column_mask] i.e.
In [11]: df.loc[:, df.sum() > 0]
Out[11]:
A C
total 5 2
# or
In [12]: df.loc[:, df.iloc[0] > 0]
Out[12]:
A C
total 5 2
answered Feb 5 at 20:27
Andy HaydenAndy Hayden
186k52434422
add a comment |
4
Use .where to set negative values to NaN and then dropna setting axis = 1:
df.where(df.gt(0)).dropna(axis=1)
A C
total 5 2
answered Feb 5 at 20:27
yatuyatu
12k31339
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
You can use, loc with boolean indexing or reindex:
df.loc[:, df.columns[(df.loc['total'] > 0)]]
OR
df.reindex(df.columns[(df.loc['total'] > 0)], axis=1)
Output:
A C
0.js 2 1
1.js 3 1
total 5 2
answered Feb 5 at 20:48
Scott BostonScott Boston
56.1k73157
add a comment |
2
You can use, loc with boolean indexing or reindex:
df.loc[:, df.columns[(df.loc['total'] > 0)]]
OR
df.reindex(df.columns[(df.loc['total'] > 0)], axis=1)
Output:
A C
0.js 2 1
1.js 3 1
total 5 2
answered Feb 5 at 20:48
Scott BostonScott Boston
56.1k73157
add a comment |
2
2
2
You can use, loc with boolean indexing or reindex:
df.loc[:, df.columns[(df.loc['total'] > 0)]]
OR
df.reindex(df.columns[(df.loc['total'] > 0)], axis=1)
Output:
A C
0.js 2 1
1.js 3 1
total 5 2
answered Feb 5 at 20:48
Scott BostonScott Boston
56.1k73157
You can use, loc with boolean indexing or reindex:
df.loc[:, df.columns[(df.loc['total'] > 0)]]
OR
df.reindex(df.columns[(df.loc['total'] > 0)], axis=1)
Output:
A C
0.js 2 1
1.js 3 1
total 5 2
answered Feb 5 at 20:48
Scott BostonScott Boston
56.1k73157
answered Feb 5 at 20:48
Scott BostonScott Boston
56.1k73157
answered Feb 5 at 20:48
Scott BostonScott Boston
56.1k73157
answered Feb 5 at 20:48
Scott BostonScott Boston
56.1k73157
56.1k73157
add a comment |
add a comment |
4
You want to use .loc[:, column_mask] i.e.
In [11]: df.loc[:, df.sum() > 0]
Out[11]:
A C
total 5 2
# or
In [12]: df.loc[:, df.iloc[0] > 0]
Out[12]:
A C
total 5 2
answered Feb 5 at 20:27
Andy HaydenAndy Hayden
186k52434422
add a comment |
4
You want to use .loc[:, column_mask] i.e.
In [11]: df.loc[:, df.sum() > 0]
Out[11]:
A C
total 5 2
# or
In [12]: df.loc[:, df.iloc[0] > 0]
Out[12]:
A C
total 5 2
answered Feb 5 at 20:27
Andy HaydenAndy Hayden
186k52434422
add a comment |
4
4
4
You want to use .loc[:, column_mask] i.e.
In [11]: df.loc[:, df.sum() > 0]
Out[11]:
A C
total 5 2
# or
In [12]: df.loc[:, df.iloc[0] > 0]
Out[12]:
A C
total 5 2
answered Feb 5 at 20:27
Andy HaydenAndy Hayden
186k52434422
You want to use .loc[:, column_mask] i.e.
In [11]: df.loc[:, df.sum() > 0]
Out[11]:
A C
total 5 2
# or
In [12]: df.loc[:, df.iloc[0] > 0]
Out[12]:
A C
total 5 2
answered Feb 5 at 20:27
Andy HaydenAndy Hayden
186k52434422
answered Feb 5 at 20:27
Andy HaydenAndy Hayden
186k52434422
answered Feb 5 at 20:27
Andy HaydenAndy Hayden
186k52434422
answered Feb 5 at 20:27
Andy HaydenAndy Hayden
186k52434422
186k52434422
add a comment |
add a comment |
4
Use .where to set negative values to NaN and then dropna setting axis = 1:
df.where(df.gt(0)).dropna(axis=1)
A C
total 5 2
answered Feb 5 at 20:27
yatuyatu
12k31339
add a comment |
4
Use .where to set negative values to NaN and then dropna setting axis = 1:
df.where(df.gt(0)).dropna(axis=1)
A C
total 5 2
answered Feb 5 at 20:27
yatuyatu
12k31339
add a comment |
4
4
4
Use .where to set negative values to NaN and then dropna setting axis = 1:
df.where(df.gt(0)).dropna(axis=1)
A C
total 5 2
answered Feb 5 at 20:27
yatuyatu
12k31339
Use .where to set negative values to NaN and then dropna setting axis = 1:
df.where(df.gt(0)).dropna(axis=1)
A C
total 5 2
answered Feb 5 at 20:27
yatuyatu
12k31339
edited Feb 5 at 20:32
answered Feb 5 at 20:27
yatuyatu
12k31339
answered Feb 5 at 20:27
yatuyatu
12k31339
answered Feb 5 at 20:27
yatuyatu
12k31339
12k31339
add a comment |
add a comment |
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2
Do you want to only keep the columns where the condition is met ALWAYS, even if the condition is not met once in another column.
– Edeki Okoh
Feb 5 at 20:26
2
Can't you transpose and do the filter?
– Andrew Naguib
Feb 5 at 20:27
Your
DataFrameis just a single row?– ALollz
Feb 5 at 20:30
not a single row
– J Oderberg
Feb 5 at 20:32
Please, add a reproducible dataframe :)
– Andrew Naguib
Feb 5 at 20:39