Selecting values in two columns based on conditions in another column (Python)

Introduction

When working with data, it’s often necessary to filter and process data based on specific conditions. In this blog post, we’ll explore how to select values in two columns based on conditions in another column using Python.

Background

The problem presented is a common scenario in data analysis and processing. The goal is to identify rows where certain conditions are met and then perform operations on those rows. This can be achieved using various techniques such as looping, conditional statements, and data manipulation libraries like Pandas.

Problem Description

Given a table with multiple variables, including the columns “number”, “lon(geo)”, and “lat(geo)”. The “number” column has different ranges of values: from 0 to x, then from 0 to y. We want to filter out the lon’s and lat’s in separate dataframes based on these conditions.

Example Table

    number     lon(geo)     lat(geo)
0    -47     66       67
1    -45     66       67
2    -44     67       67
3    -41     67       67
4    -38     67       67
5    -37     67       67
0    -8      38       40
1    -7      39       39
2    -6      39       39
3    -5      40       41
4    -5      41       41
0    .        .
.    .        .

Initial Attempts

The initial attempt was to use the following Python code:

for i in range(1,len(number)):
    break    

while (float(i) for i in number > 0):
    lon = print(lon)
    lat = print(lat)
    break

This code is not efficient and does not meet the requirements.

Solution

A more effective approach is to use a Pandas DataFrame, which provides an efficient way to manipulate data based on conditions. We’ll split the DataFrame into separate DataFrames for each range of values in the “number” column and then perform operations on those DataFrames.

Step 1: Importing Libraries and Loading Data

We’ll start by importing the necessary libraries and loading the data from a CSV file:

import pandas as pd

# Load the CSV file
df = pd.read_csv('test1.csv', delimiter=";")

Step 2: Identifying Zeros in the “number” Column

Next, we’ll identify the indices of zeros in the “number” column using the following code:

zeros = df.index[df['number'] == 0].tolist()

This will give us a list of indices where the value in the “number” column is zero.

Step 3: Creating Separate DataFrames

We’ll then create separate DataFrames for each range of values in the “number” column using the following code:

new_dfs = []

for i in range(0,len(zeros)):
    try:
        df_aux = df.iloc[zeros[i]:zeros[i+1]]
    except:
        df_aux = df.iloc[zeros[i]:len(df)]
    
    new_dfs.append(df_aux)

This code will create a list of DataFrames, where each DataFrame corresponds to a range of values in the “number” column.

Step 4: Processing Separate DataFrames

Finally, we can process the separate DataFrames as needed. For example, we might want to perform some calculations or print out certain columns:

for i, df_aux in enumerate(new_dfs):
    # Perform operations on the current DataFrame
    print(f"Processing DataFrame {i+1}...")
    
    # Print specific columns
    print(df_aux['lon(geo)'])
    print(df_aux['lat(geo)'])

This code will process each separate DataFrame and perform any desired calculations or printing.

Conclusion

In this blog post, we explored how to select values in two columns based on conditions in another column using Python. We used Pandas DataFrames to efficiently manipulate data and create separate DataFrames for each range of values in the “number” column. This approach can be useful when working with data that has complex patterns or relationships between columns.

Code

The complete code for this example is as follows:

import pandas as pd

# Load the CSV file
df = pd.read_csv('test1.csv', delimiter=";")

# Identify zeros in the "number" column
zeros = df.index[df['number'] == 0].tolist()

# Create separate DataFrames
new_dfs = []

for i in range(0,len(zeros)):
    try:
        df_aux = df.iloc[zeros[i]:zeros[i+1]]
    except:
        df_aux = df.iloc[zeros[i]:len(df)]
    
    new_dfs.append(df_aux)

# Process separate DataFrames
for i, df_aux in enumerate(new_dfs):
    print(f"Processing DataFrame {i+1}...")
    
    # Print specific columns
    print(df_aux['lon(geo)'])
    print(df_aux['lat(geo)'])

This code can be copied and pasted into a Python environment to test the solution.

Last modified on 2024-12-27