Use Pandas to Calculate Stats from an Imported CSV file

May 29, 2021

Pandas is a powerful Python package that can be used to perform statistical analysis. In this guide, you’ll see how to use Pandas to calculate stats from an imported CSV file.

The Example

To demonstrate how to calculate stats from an imported CSV file, let’s review a simple example with the following dataset:

Name Salary Country
Dan 40000 USA
Elizabeth 32000 Brazil
Jon 45000 Italy
Maria 54000 USA
Mark 72000 USA
Bill 62000 Brazil
Jess 92000 Italy
Julia 55000 USA
Jeff 35000 Italy
Ben 48000 Brazil

Steps to Calculate Stats from an Imported CSV File

Step 1: Copy the Dataset into a CSV file

To begin, you’ll need to copy the above dataset into a CSV file. Then rename the CSV file as stats.

Step 2: Import the CSV File into Python

Next, you’ll need to import the CSV file into Python using this template:

import pandas as pd
df = pd.read_csv (r'Path where the CSV file is stored\File name.csv')
print (df)

Here is an example of a path where the CSV file is stored:

 C:\Users\Ron\Desktop\stats.csv

So the complete code to import the stats CSV file is captured below (note that you’ll need to modify the path to reflect the location where the CSV file is stored on your computer):

import pandas as pd
df = pd.read_csv (r'C:\Users\Ron\Desktop\stats.csv')
print (df)

Once you run the code in Python (adjusted to your path), you’ll get the following DataFrame:

        Name  Salary Country
0        Dan   40000     USA
1  Elizabeth   32000  Brazil
2        Jon   45000   Italy
3      Maria   54000     USA
4       Mark   72000     USA
5       Bill   62000  Brazil
6       Jess   92000   Italy
7      Julia   55000     USA
8       Jeff   35000   Italy
9        Ben   48000  Brazil

Step 3: Use Pandas to Calculate Stats from an Imported CSV File

For the final step, the goal is to calculate the following statistics using the Pandas package:

  • Mean salary
  • Total sum of salaries
  • Maximum salary
  • Minimum salary
  • Count of salaries
  • Median salary
  • Standard deviation of salaries
  • Variance of of salaries

In addition, we’ll also do some grouping calculations:

  • Sum of salaries, grouped by the Country column
  • Count of salaries, grouped by the Country column

Once you’re ready, run the code below in order to calculate the stats from the imported CSV file using Pandas. As indicated earlier, you’ll need to change the path name (2nd row in the code) to reflect the location where the CSV file is stored on your computer.

import pandas as pd
df = pd.read_csv (r'C:\Users\Ron\Desktop\stats.csv') 

# block 1 - simple stats
mean1 = df['Salary'].mean()
sum1 = df['Salary'].sum()
max1 = df['Salary'].max()
min1 = df['Salary'].min()
count1 = df['Salary'].count()
median1 = df['Salary'].median() 
std1 = df['Salary'].std() 
var1 = df['Salary'].var()  

# block 2 - group by
groupby_sum1 = df.groupby(['Country']).sum() 
groupby_count1 = df.groupby(['Country']).count()

# print block 1
print ('Mean salary: ' + str(mean1))
print ('Sum of salaries: ' + str(sum1))
print ('Max salary: ' + str(max1))
print ('Min salary: ' + str(min1))
print ('Count of salaries: ' + str(count1))
print ('Median salary: ' + str(median1))
print ('Std of salaries: ' + str(std1))
print ('Var of salaries: ' + str(var1))

# print block 2
print ('Sum of values, grouped by the Country: ' + str(groupby_sum1))
print ('Count of values, grouped by the Country: ' + str(groupby_count1))

After you run the code in Python, you’ll get the following results:

Mean salary: 53500.0
Sum of salaries: 535000
Max salary: 92000
Min salary: 32000
Count of salaries: 10
Median salary: 51000.0
Std of salaries: 18222.391598128816
Var of salaries: 332055555.5555556
Sum of values, grouped by the Country:
Country        
Brazil   142000
Italy    172000
USA      221000
Count of values, grouped by the Country:
Country              
Brazil      3       3
Italy       3       3
USA         4       4

You just saw how to calculate simple stats using Pandas. You may also want to check the Pandas documentation to learn more about the power of this great library!