First, let us load Pandas.
import pandas as pd
We will use gapminder dataset to change the column orders. Let us load gapminder dataset directly from cmdlinetips.com’s github page.
data_link = "https://raw.githubusercontent.com/cmdlinetips/data/master/gapminder-FiveYearData.csv"
df = pd.read_csv(data_link)
Our data looks like this.
df.head() country year pop continent lifeExp gdpPercap 0 Afghanistan 1952 8425333.0 Asia 28.801 779.445314 1 Afghanistan 1957 9240934.0 Asia 30.332 820.853030 2 Afghanistan 1962 10267083.0 Asia 31.997 853.100710 3 Afghanistan 1967 11537966.0 Asia 34.020 836.197138 4 Afghanistan 1972 13079460.0 Asia 36.088 739.981106
In this dataframe the columns are ordered as
df.columns.tolist() ['country', 'year', 'pop', 'continent', 'lifeExp', 'gdpPercap']
Let us say we want to change the column orders in a specific way. Here is an example order that we want.
col_order = ["continent","country", "lifeExp","pop","gdpPercap"]
Change Order of Columns in a Dataframe
One of the ways to change the column orders is to use the list containing the new column order and subset the dataframe as shown below.
df_new = df[col_order]
The resulting dataframe will contain the columns in the order we want.
df_new.head() continent country lifeExp pop gdpPercap 0 Asia Afghanistan 28.801 8425333.0 779.445314 1 Asia Afghanistan 30.332 9240934.0 820.853030 2 Asia Afghanistan 31.997 10267083.0 853.100710 3 Asia Afghanistan 34.020 11537966.0 836.197138 4 Asia Afghanistan 36.088 13079460.0 739.981106
Change Column Orders with Pandas reindex()
Another way to change the order of columns of a Pandas dataframe is to use Pandas reindex() function and use the list with the new column order we need.
Here we need to specify that we are re-indexing the columns, by using the argument “columns”.
df_new = df.reindex(columns=col_order)
df_new.head() continent country lifeExp pop gdpPercap 0 Asia Afghanistan 28.801 8425333.0 779.445314 1 Asia Afghanistan 30.332 9240934.0 820.853030 2 Asia Afghanistan 31.997 10267083.0 853.100710 3 Asia Afghanistan 34.020 11537966.0 836.197138 4 Asia Afghanistan 36.088 13079460.0 739.981106
If you are interested changing just one of the column’s position. check out Pandas insert() function to move a column to a specific location.
