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I have 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:
df['mean'] = df.mean(1) 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?
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You need to create a new list of your columns in the desired order, then use df = df[cols] to rearrange the columns in this new order.
Reorder Columns using Pandas . Another way to reorder columns is to use the Pandas . reindex() method. This allows you to pass in the columns= parameter to pass in the order of columns that you want to use.
Reordering or Rearranging the column of dataframe in pandas python can be done by using reindex function. In order to reorder or rearrange the column in pandas python. We will be different methods. To reorder the column in ascending order we will be using Sort() function.
In order to Rearrange or reorder the column in pyspark we will be using select function. To reorder the column in ascending order we will be using Sorted function. To reorder the column in descending order we will be using Sorted function with an argument reverse =True. We also rearrange the column by position.
You could also do something like this:
df = df[['mean', '0', '1', '2', '3']] You can get the list of columns with:
cols = list(df.columns.values) The output will produce:
['0', '1', '2', '3', 'mean'] ...which is then easy to rearrange manually before dropping it into the first function
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One easy way would be to reassign the dataframe with a list of the columns, rearranged as needed.
This is what you have now:
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:
In [12]: cols = cols[-1:] + cols[:-1] In [13]: cols Out[13]: ['mean', 0L, 1L, 2L, 3L, 4L] Then reorder the dataframe like this:
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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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