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How do I change the order of DataFrame columns?
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Timmie asked:
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For the following DataFrame(df):
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import numpy as np import pandas as pd # df = pd.DataFrame(np.random.rand(10, 5))
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I added a new column:
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df['mean'] = df.mean(1)
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How do I move the mean column to the beginning? Or how to take the mean column as the first column and move back without changing the order of other columns?
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Answers:
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Aman - vote: 1144
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A simple way is to change the arrangement of the dataframe in the form of a list. You can arrange the columns in different ways as needed.
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For example, for the following dataframe:
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In [6]: df Out[6]: 0 1 2 3 4 mean 0 0.445598 0.173835 0.343415 0.682252 0.582616 0.445543 1 0.881592 0.696942 0.702232 0.696724 0.373551 0.670208 2 0.662527 0.955193 0.131016 0.609548 0.804694 0.632596 3 0.260919 0.783467 0.593433 0.033426 0.512019 0.436653 4 0.131842 0.799367 0.182828 0.683330 0.019485 0.363371 5 0.498784 0.873495 0.383811 0.699289 0.480447 0.587165 6 0.388771 0.395757 0.745237 0.628406 0.784473 0.588529 7 0.147986 0.459451 0.310961 0.706435 0.100914 0.345149 8 0.394947 0.863494 0.585030 0.565944 0.356561 0.553195 9 0.689260 0.865243 0.136481 0.386582 0.730399 0.561593 # In [7]: cols = df.columns.tolist() # In [8]: cols Out[8]: [0L, 1L, 2L, 3L, 4L, 'mean'] -
Rearrange these cols the way you want them to. Here is how I move the last column element to the first column:
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In [12]: cols = cols[-1:] + cols[:-1] # In [13]: cols Out[13]: ['mean', 0L, 1L, 2L, 3L, 4L]
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The sorted dataframe is as follows:
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In [16]: df = df[cols] # OR df = df.ix[:, cols] # In [17]: df Out[17]: mean 0 1 2 3 4 0 0.445543 0.445598 0.173835 0.343415 0.682252 0.582616 1 0.670208 0.881592 0.696942 0.702232 0.696724 0.373551 2 0.632596 0.662527 0.955193 0.131016 0.609548 0.804694 3 0.436653 0.260919 0.783467 0.593433 0.033426 0.512019 4 0.363371 0.131842 0.799367 0.182828 0.683330 0.019485 5 0.587165 0.498784 0.873495 0.383811 0.699289 0.480447 6 0.588529 0.388771 0.395757 0.745237 0.628406 0.784473 7 0.345149 0.147986 0.459451 0.310961 0.706435 0.100914 8 0.553195 0.394947 0.863494 0.585030 0.565944 0.356561 9 0.561593 0.689260 0.865243 0.136481 0.386582 0.730399
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freddygv - vote: 675
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This will:
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df = df[['mean', '0', '1', '2', '3']]
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The following code is used to get a list of columns:
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cols = list(df.columns.values)
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Output:
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['0', '1', '2', '3', 'mean']
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It's very convenient.
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fixxxer - vote: 367
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Just process the column names in the order you want them to:
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In [39]: df Out[39]: 0 1 2 3 4 mean 0 0.172742 0.915661 0.043387 0.712833 0.190717 1 1 0.128186 0.424771 0.590779 0.771080 0.617472 1 2 0.125709 0.085894 0.989798 0.829491 0.155563 1 3 0.742578 0.104061 0.299708 0.616751 0.951802 1 4 0.721118 0.528156 0.421360 0.105886 0.322311 1 5 0.900878 0.082047 0.224656 0.195162 0.736652 1 6 0.897832 0.558108 0.318016 0.586563 0.507564 1 7 0.027178 0.375183 0.930248 0.921786 0.337060 1 8 0.763028 0.182905 0.931756 0.110675 0.423398 1 9 0.848996 0.310562 0.140873 0.304561 0.417808 1 # In [40]: df = df[['mean', 4,3,2,1]] -
After processing, the 'mean' column will be at the beginning:
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In [41]: df Out[41]: mean 4 3 2 1 0 1 0.190717 0.712833 0.043387 0.915661 1 1 0.617472 0.771080 0.590779 0.424771 2 1 0.155563 0.829491 0.989798 0.085894 3 1 0.951802 0.616751 0.299708 0.104061 4 1 0.322311 0.105886 0.421360 0.528156 5 1 0.736652 0.195162 0.224656 0.082047 6 1 0.507564 0.586563 0.318016 0.558108 7 1 0.337060 0.921786 0.930248 0.375183 8 1 0.423398 0.110675 0.931756 0.182905 9 1 0.417808 0.304561 0.140873 0.310562
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How to change the order of DataFrame columns?
-
Timmie asked:
-
I have the following DataFrame (df):
For the following DataFrame(df): -
import numpy as np import pandas as pd # df = pd.DataFrame(np.random.rand(10, 5))
-
I add more column(s) by assignment:
I added a new column:
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df['mean'] = df.mean(1)
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How can I move the column mean to the front, i.e. set it as first column leaving the order of the other columns untouched?
How do I move the mean column to the beginning? Or how to take the mean column as the first column and move back without changing the order of other columns?
-
-
Answers:
-
Aman - vote: 1144
-
One easy way would be to reassign the dataframe with a list of the columns, rearranged as needed.
A simple way is to change the arrangement of the dataframe in the form of a list. You can arrange the columns in different ways as needed. -
This is what you have now:
For example, for the following dataframe: -
In [6]: df Out[6]: 0 1 2 3 4 mean 0 0.445598 0.173835 0.343415 0.682252 0.582616 0.445543 1 0.881592 0.696942 0.702232 0.696724 0.373551 0.670208 2 0.662527 0.955193 0.131016 0.609548 0.804694 0.632596 3 0.260919 0.783467 0.593433 0.033426 0.512019 0.436653 4 0.131842 0.799367 0.182828 0.683330 0.019485 0.363371 5 0.498784 0.873495 0.383811 0.699289 0.480447 0.587165 6 0.388771 0.395757 0.745237 0.628406 0.784473 0.588529 7 0.147986 0.459451 0.310961 0.706435 0.100914 0.345149 8 0.394947 0.863494 0.585030 0.565944 0.356561 0.553195 9 0.689260 0.865243 0.136481 0.386582 0.730399 0.561593 # In [7]: cols = df.columns.tolist() # In [8]: cols Out[8]: [0L, 1L, 2L, 3L, 4L, 'mean'] -
Rearrange cols in any way you want. This is how I moved the last element to the first position:
Rearrange these cols the way you want them to. Here is how I move the last column element to the first column: -
In [12]: cols = cols[-1:] + cols[:-1] # In [13]: cols Out[13]: ['mean', 0L, 1L, 2L, 3L, 4L]
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Then reorder the dataframe like this:
The sorted dataframe is as follows: -
In [16]: df = df[cols] # OR df = df.ix[:, cols] # In [17]: df Out[17]: mean 0 1 2 3 4 0 0.445543 0.445598 0.173835 0.343415 0.682252 0.582616 1 0.670208 0.881592 0.696942 0.702232 0.696724 0.373551 2 0.632596 0.662527 0.955193 0.131016 0.609548 0.804694 3 0.436653 0.260919 0.783467 0.593433 0.033426 0.512019 4 0.363371 0.131842 0.799367 0.182828 0.683330 0.019485 5 0.587165 0.498784 0.873495 0.383811 0.699289 0.480447 6 0.588529 0.388771 0.395757 0.745237 0.628406 0.784473 7 0.345149 0.147986 0.459451 0.310961 0.706435 0.100914 8 0.553195 0.394947 0.863494 0.585030 0.565944 0.356561 9 0.561593 0.689260 0.865243 0.136481 0.386582 0.730399
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freddygv - vote: 675
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You could also do something like this:
This will: -
df = df[['mean', '0', '1', '2', '3']]
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You can get the list of columns with:
The following code is used to get a list of columns: -
cols = list(df.columns.values)
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The output will produce:
Output: -
['0', '1', '2', '3', 'mean']
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...which is then easy to rearrange manually before dropping it into the first function
It's very convenient.
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fixxxer - vote: 367
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Just assign the column names in the order you want them:
Just process the column names in the order you want them to: -
In [39]: df Out[39]: 0 1 2 3 4 mean 0 0.172742 0.915661 0.043387 0.712833 0.190717 1 1 0.128186 0.424771 0.590779 0.771080 0.617472 1 2 0.125709 0.085894 0.989798 0.829491 0.155563 1 3 0.742578 0.104061 0.299708 0.616751 0.951802 1 4 0.721118 0.528156 0.421360 0.105886 0.322311 1 5 0.900878 0.082047 0.224656 0.195162 0.736652 1 6 0.897832 0.558108 0.318016 0.586563 0.507564 1 7 0.027178 0.375183 0.930248 0.921786 0.337060 1 8 0.763028 0.182905 0.931756 0.110675 0.423398 1 9 0.848996 0.310562 0.140873 0.304561 0.417808 1 # In [40]: df = df[['mean', 4,3,2,1]] -
Now, 'mean' column comes out in the front:
After processing, the 'mean' column will be at the beginning: -
In [41]: df Out[41]: mean 4 3 2 1 0 1 0.190717 0.712833 0.043387 0.915661 1 1 0.617472 0.771080 0.590779 0.424771 2 1 0.155563 0.829491 0.989798 0.085894 3 1 0.951802 0.616751 0.299708 0.104061 4 1 0.322311 0.105886 0.421360 0.528156 5 1 0.736652 0.195162 0.224656 0.082047 6 1 0.507564 0.586563 0.318016 0.558108 7 1 0.337060 0.921786 0.930248 0.375183 8 1 0.423398 0.110675 0.931756 0.182905 9 1 0.417808 0.304561 0.140873 0.310562
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