Minimum Value in Each Group – Pandas Groupby

You can use Pandas groupby to group the underlying data on one or more columns and estimate useful statistics like count, mean, median, min, max etc. In this tutorial, we will look at how to get the minimum value for each group in pandas groupby with the help of some examples.

Minimum of each group in pandas groupby.

To get the minimum value of each group, you can directly apply the pandas min() function to the selected column(s) from the result of pandas groupby. The following is a step-by-step guide of what you need to do.

  1. Group the dataframe on the column(s) you want.
  2. Select the field(s) for which you want to estimate the minimum.
  3. Apply the pandas min() function directly or pass ‘min’ to the agg() function.

The following is the syntax –

# groupby columns on Col1 and estimate the minimum value of column Col2 for each group
df.groupby([Col1])[Col2].min()
# alternatively, you can pass 'min' to the agg() function
df.groupby([Col1])[Col2].agg('min')

Let’s look at some examples of using the above syntax to get the minimum value for each group in pandas. First, we will create a sample dataframe that we will be using throughout this tutorial.

import pandas as pd

# create a dataframe of car models by two companies
df = pd.DataFrame({
    'Company': ['A', 'A', 'A', 'B', 'B', 'B', 'B'],
    'Model': ['A1', 'A2', 'A3', 'B1', 'B2', 'B3', 'B4'],
    'Year': [2019, 2020, 2021, 2018, 2019, 2020, 2021],
    'Transmission': ['Manual', 'Automatic', 'Automatic', 'Manual', 'Automatic', 'Automatic', 'Manual'],
    'EngineSize': [1.4, 2.0, 1.4, 1.5, 2.0, 1.5, 1.5],
    'MPG': [55.4, 67.3, 58.9, 52.3, 64.2, 68.9, 83.1]
})
# display the dataframe
df

Output:

Dataframe of car models and their specifications from two car companies

We now have a dataframe containing the specifications of the car models by two different companies. The “EngineSize” column is the size of the engine in litres and the “MPG” column is the mileage of the car in miles-per-gallon.

Let’s get the minimum value of mileage “MPG” for each “Company” in the dataframe df. To do this, group the dataframe on the column “Company”, select the “MPG” column, and then apply the min() function.

# min MPG for each Company
df.groupby('Company')['MPG'].min()

Output:

Company
A    55.4
B    52.3
Name: MPG, dtype: float64

You can see that minimum mileage “MPG” for company “B” is less than that of company “A”.

Alternatively, you can also use the pandas agg() function on the resulting groups.

# min MPG for each Company
df.groupby('Company')['MPG'].agg('min')

Output:

Company
A    55.4
B    52.3
Name: MPG, dtype: float64

We get the same result as above.

You can also group the data on multiple columns (to get more granular groups) and then compute the min for each group. For example, let’s group the data on “Company” and “Transmission” and get the minimum “MPG” for each group.

# min MPG for each Company at a transmission level
df.groupby(['Company', 'Transmission'])['MPG'].min()

Output:

Company  Transmission
A        Automatic       58.9
         Manual          55.4
B        Automatic       64.2
         Manual          52.3
Name: MPG, dtype: float64

We can also get the minimum values for more than one columns at a time for each group resulting from groupby. For example, let’s get the minimum value of mileage “MPG” and “EngineSize” for each “Company” in the dataframe df.

# min MPG and EngineSize for each Company
df.groupby('Company')[['MPG', 'EngineSize']].min()

Output:

Minimum mileage "MPG" and "EngineSize" for each company.

Here we selected the columns that we wanted to compute the minimum on from the resulting groupby object and then applied the min() function. We already know that the minimum “MPG” is smaller for company “B”. Here we additionally find that the minimum “EngineSize” is smaller for company “A”.

Let’s now do the same thing using the pandas agg() function.

# min MPG and EngineSize for each Company
df.groupby('Company')[['MPG', 'EngineSize']].agg('min')

Output:

Minimum mileage "MPG" and "EngineSize" for each company.

We get the same result as above.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5


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