Searching for Rows in a Pandas DataFrame using Various Search Options

In this article, we will explore how to search for rows in a Pandas DataFrame using various options such as searching by atomic symbol, atomic number, atomic weight, English name, and Dutch name.

Introduction

Pandas is a powerful library in Python that provides data structures and functions for efficiently handling structured data, including tabular data such as spreadsheets and SQL tables. One of the most commonly used functions in Pandas is read_csv(), which allows you to read a CSV file into a DataFrame.

However, when working with large datasets or complex data queries, it can be challenging to search for specific rows or values within the dataset. In this article, we will demonstrate how to search for rows in a Pandas DataFrame using various options, including searching by atomic symbol, atomic number, atomic weight, English name, and Dutch name.

Background

The code provided is written in Python and uses the Pandas library to read a CSV file into a DataFrame. The DataFrame contains data on various elements, including their atomic symbol, atomic number, atomic weight, English name, and Dutch name.

import pandas as pd

# Create a sample DataFrame
data = {
    'atSym': ['H', 'He', 'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne', 'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'Cl', 'Ar', 'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu'],
    'atNum': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],
    'atWeight': [1.008, 4.0026, 6.94, 9.0122, 10.81, 12.011, 14.007, 15.999, 18.998, 20.180, 22.990, 24.305, 26.982, 28.085, 30.974, 32.06, 35.45, 39.948, 39.098, 40.078, 44.956, 47.867, 50.942, 51.996, 54.938, 55.845, 58.933, 58.693, 63.546],
    'ENname': ['hydrogen', 'helium', 'lithium', 'beryllium', 'boron', 'carbon', 'nitrogen', 'oxigen', 'fluorine', 'neon', 'sodium', 'magnesium', 'aluminium', 'silicon', 'phosphorus', 'sulfur', 'chlorine', 'argon', 'potassium', 'calcium', 'scandium', 'titanium', 'vanadium', 'chromium', 'manganese', 'iron', 'cobalt', 'nickel', 'copper'],
    'NLname': ['waterstof', 'helium', 'lithium', 'beryllium', 'boor', 'koolstof', 'stikstof', 'zuurstof', 'fluor', 'neon', 'natrium', 'magnesium', 'aluminium', 'silicium', 'fosfor', 'zwavel', 'chloor', 'argon', 'kalium', 'calcium', 'scandium', 'titanium', 'vanadium', 'chroom', 'mangaan', 'ijzer', 'kobalt', 'nikkel', 'koper']
}
df = pd.DataFrame(data)

Search Options

There are several search options that can be used to find specific rows in a DataFrame. These include:

• Atomic Symbol: Searching for rows based on the atomic symbol of an element.
• Atomic Number: Searching for rows based on the atomic number of an element.
• Atomic Weight: Searching for rows based on the atomic weight of an element.
• English Name: Searching for rows based on the English name of an element.
• Dutch Name: Searching for rows based on the Dutch name of an element.

Code

Here is a sample code snippet that demonstrates how to search for rows in a DataFrame using various options:

# Mapping between search options and columns in the DataFrame
d = {
    'atomic symbol': 'atSym',
    'atomic number': 'atNum',
    'atomic weight': 'atWeight',
    'english name': 'ENname',
    'dutch name': 'NLname',
}

while True:
    # Search Options
    print("Search options: ")
    print("Search by Atomic Symbol")
    print("Search by Atomic Number")
    print("Search by Atomic Weight")
    print("Search by English Name")
    print("Search by Dutch Name")

    # get search option and select the right column to search on
    # we're converting the column to lowercase strings, so that
    # we can conveniently find it later
    searchOption = input().lower()
    df_ix = df[d[searchOption]].astype(str).str.lower()

    # get value to search for, convert to lowercase and find row
    searchValue = input().lower()
    row = df[df_ix==searchValue]

    # rename columns to human-readable based on our mapping and print
    print("=====================================")
    print(row.rename(columns={v: k for k, v in d.items()}))
    print("=====================================")

Output

The output of the code snippet above will be a DataFrame containing the rows that match the search value. For example:

Search options: 
Search by Atomic Symbol
Search by Atomic Number
Search by Atomic Weight
Search by English Name
Search by Dutch Name
 english name
 helium
=====================================
  atomic symbol  atomic number  atomic weight english name dutch name
1  He             2             4.0            helium       helium   
=====================================

This shows that the row with the atomic symbol ‘He’ and atomic number 2 was found in the DataFrame.

Conclusion

In this article, we demonstrated how to search for rows in a Pandas DataFrame using various options such as searching by atomic symbol, atomic number, atomic weight, English name, and Dutch name. We also provided sample code snippets that can be used to implement these search options in real-world applications. By following the steps outlined in this article, developers can create efficient and effective data queries using Pandas and Python.

Step-by-Step Guide

Here is a step-by-step guide to implementing the search options:

1. Create a sample DataFrame with relevant columns.
2. Define a mapping between search options and columns in the DataFrame.
3. Use a while loop to continuously prompt the user for input until they choose to exit.
4. Inside the loop, use the input() function to get the search option from the user.
5. Convert the column names to lowercase strings using the astype(str).str.lower() method.
6. Get the value to search for from the user and convert it to lowercase.
7. Use boolean indexing to find rows that match the search value in the DataFrame.
8. Rename the columns to human-readable names based on the mapping defined earlier.
9. Print the resulting DataFrame.

By following these steps, developers can implement efficient and effective data queries using Pandas and Python.

Last modified on 2023-11-30