Automatically calculate value over several tables with python including filteri

When working with multiple tables in Python, it can be quite challenging to automatically calculate values while also applying filters. However, there are several ways to solve this problem. In this article, we will explore three different approaches to tackle this issue.

Approach 1: Using Pandas

Pandas is a powerful library in Python that provides data manipulation and analysis tools. It offers a convenient way to work with tabular data, making it an excellent choice for solving this problem.

import pandas as pd

# Read the tables into pandas dataframes
table1 = pd.read_csv('table1.csv')
table2 = pd.read_csv('table2.csv')
table3 = pd.read_csv('table3.csv')

# Apply filters to the tables
filtered_table1 = table1[table1['column_name'] == 'filter_value']
filtered_table2 = table2[table2['column_name'] == 'filter_value']
filtered_table3 = table3[table3['column_name'] == 'filter_value']

# Perform calculations on the filtered tables
result = filtered_table1['column_name'].sum() + filtered_table2['column_name'].mean() - filtered_table3['column_name'].max()


In this approach, we use the pandas library to read the tables into dataframes. We then apply filters to each table using boolean indexing. Finally, we perform the desired calculations on the filtered tables and obtain the result.

Approach 2: Using SQL

If you are familiar with SQL, you can leverage its power to solve this problem. Python provides several libraries that allow you to execute SQL queries on your tables.

import sqlite3

# Connect to a SQLite database
conn = sqlite3.connect('database.db')

# Create a cursor object
cursor = conn.cursor()

# Execute SQL queries with filters
cursor.execute("SELECT SUM(column_name) FROM table1 WHERE column_name = 'filter_value'")
result1 = cursor.fetchone()[0]

cursor.execute("SELECT AVG(column_name) FROM table2 WHERE column_name = 'filter_value'")
result2 = cursor.fetchone()[0]

cursor.execute("SELECT MAX(column_name) FROM table3 WHERE column_name = 'filter_value'")
result3 = cursor.fetchone()[0]

# Perform calculations on the results
result = result1 + result2 - result3


In this approach, we connect to a SQLite database using the sqlite3 library. We then execute SQL queries with the desired filters and retrieve the results. Finally, we perform the calculations on the obtained results to get the final output.

Approach 3: Using NumPy

If you prefer a more mathematical approach, you can utilize the NumPy library to solve this problem. NumPy provides efficient numerical operations on arrays, making it suitable for calculations involving multiple tables.

import numpy as np

# Read the tables into NumPy arrays
table1 = np.genfromtxt('table1.csv', delimiter=',')
table2 = np.genfromtxt('table2.csv', delimiter=',')
table3 = np.genfromtxt('table3.csv', delimiter=',')

# Apply filters to the arrays
filtered_table1 = table1[table1[:, column_index] == filter_value]
filtered_table2 = table2[table2[:, column_index] == filter_value]
filtered_table3 = table3[table3[:, column_index] == filter_value]

# Perform calculations on the filtered arrays
result = np.sum(filtered_table1[:, column_index]) + np.mean(filtered_table2[:, column_index]) - np.max(filtered_table3[:, column_index])


In this approach, we use the NumPy library to read the tables into arrays. We then apply filters to each array using boolean indexing. Finally, we perform the desired calculations on the filtered arrays and obtain the result.

After considering these three approaches, it is evident that using Pandas provides the most straightforward and concise solution. Pandas offers a high-level interface for data manipulation, making it easier to read, filter, and perform calculations on multiple tables. Therefore, Approach 1 using Pandas is the recommended option for solving this Python question.

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