I know how to read in the data frame in Pandas and do the basic manipulation, but how do I populate the order column based on the ID column? For instance, if bike occurs twice such as seen below, how can I populate the order column with 1 for the first occurrence and then 2 for the second occurrence and then do the same for car.

    ID      Color       Order?????
    bike    red         1
    bike    black       2
    car     green       1
    car     orange      2
    car     blue        3
  • $\begingroup$ stackoverflow.com/questions/26720916/… PS. As a good practice, when you post a question like this, it's easier to answer if you provide some short code to create the dataframe. Also, these types of programming questions are better suited for StackOverflow. $\endgroup$ Oct 4, 2018 at 21:40

2 Answers 2


Welcome to Data Science! Here I create your data frame and show one way to create the column you need. I use numpy in addition to pandas:

import pandas as pd
import numpy as np

# Create df
ID = ['bike', 'bike', 'car', 'car', 'car']
Color = ['red', 'black', 'green', 'orange', 'blue']
df = pd.DataFrame(data={'ID': ID, 'Color': Color})

Add a new column called 'Order', with counts based on each group - these are mini dataframes that contain only one of the ID values only. This makes use of the groupby method on a Pandas dataframe.

df['Order'] = df.groupby('ID').transform(lambda x: 1 + np.arange(len(x)))

The transform method takes a function and applies it to each group. I use an anonymous function (just a function with no name), also called lambda functions, using the keyword lambda.

The result:

    Color    ID  Order
0     red  bike      1
1   black  bike      2
2   green   car      1
3  orange   car      2
4    blue   car      3

As anymous.asker mentioned, it would be helpful in the future if you post the code that creates your dataframe!


This can be done with GroupBy.cumcount (pandas package):

df['Order'] = df.groupby('ID').cumcount()



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