Using Python Pandas for Analysis: Calculating Total Crop Area and Number of Farmers per Survey Number

In this article, we will explore how to use the popular Python library Pandas to perform calculations on a dataset. Specifically, we will focus on calculating the total crop area and number of farmers per survey number.

We start with a sample dataset containing information about 50,000 farmers who are growing crops in various villages. Each farmer is assigned a unique survey number, and each farm has an associated land area.

Understanding the Dataset

The dataset consists of five columns:

Name: The name of the farmer.
Village: The village where the farmer lives.
Survey_no: A unique identifier for each survey.
Land_Area: The total land area of the farm.

Here is an example of what the dataset might look like:

| Name    | Village     | Survey_no | Land_Area |
| ---      | ---         | ---        | ---        |
| Farmer_1 | Village_1   | 26        | 0.33       |
| Farmer_1 | Village_1   | 26        | 0.40       |
| Farmer_2 | Village_1   | 26        | 0.30       |
| Farmer_3 | Village_1   | 26        | 0.52       |

Preparing the Dataset for Analysis

To perform calculations on the dataset, we need to prepare it by converting the Village and Survey_no columns into a format that can be used in groupby operations.

Here is an example of how you might do this:

# Set 'Village' and 'Survey_no' as indices
df.set_index(['Village', 'Survey_no'], inplace=True)

# Group by 'Village' and 'Survey_no'
grouped_df = df.groupby(['Village', 'Survey_no'])

# Calculate the total land area for each group
total_land_area = grouped_df['Land_Area'].sum()

# Print the result
print(total_land_area)

Calculating Total Crop Area per Survey Number

One way to calculate the total crop area per survey number is to use the groupby function with a cumcount operation.

Here is an example of how you might do this:

# Group by 'Village' and 'Survey_no'
df_grouped = df.groupby(['Village', 'Survey_no'])

# Calculate the cumulative count for each group
cum_count = df_grouped['Name'].cumcount() + 1

# Use the cumulative count to create a new index column
new_index = cum_count.name

# Group by the new index column and calculate the sum of 'Land_Area'
total_land_area = df_grouped['Land_Area'].sum().reset_index()

# Rename the columns
total_land_area.columns = ['Village', 'Survey_no', 'Total_Land_Area']

# Print the result
print(total_land_area)

Calculating Number of Farmers per Survey Number

To calculate the number of farmers per survey number, we can use a similar approach to that used for calculating total crop area.

Here is an example of how you might do this:

# Group by 'Village' and 'Survey_no'
df_grouped = df.groupby(['Village', 'Survey_no'])

# Calculate the first occurrence for each group
first_occurrence = df_grouped['Name'].groupby(level=0).transform('min')

# Rename the columns
result.columns = ['Village', 'Survey_no', 'Number_of_Farmers']

# Print the result
print(result)

Combining Multiple Calculations

Once you have performed individual calculations, you can combine them into a single dataframe using the groupby function.

Here is an example of how you might do this:

# Group by 'Village' and 'Survey_no'
df_grouped = df.groupby(['Village', 'Survey_no'])

# Calculate multiple columns
result = df_grouped['Land_Area'].sum().reset_index()
result.columns = ['Village', 'Survey_no', 'Total_Land_Area']

# Print the result
print(result)

result = df_grouped['Name'].first().reset_index()
result.columns = ['Village', 'Survey_no', 'Number_of_Farmers']

# Print the result
print(result)

Conclusion

In this article, we explored how to use the Python Pandas library for analysis. Specifically, we focused on calculating total crop area and number of farmers per survey number.

By using the groupby function with a cumulative count operation, you can perform multiple calculations in a single step.

Whether you are working with large datasets or just performing simple calculations, Pandas is an essential tool for any data analysis task.

Last modified on 2025-01-07