Ahmed Dawoud

Ready to transform your datasets into the perfect shape for analysis? Let’s explore 5 powerful Pandas functions that will reshape your data like magic!

1. Melting Data from Wide to Long: df.melt()

• Turn columns into rows: Ideal for analyzing data with multiple observations per record.
• Example:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'year': [2000, 2000], 'artist': ['2 Pac', '2Ge+her'],
                   'track': ['Baby Don't Cry', 'The Hardest Part Of Breaking Up'],
                   'wk1': [87, 91], 'wk2': [82, 87], 'wk3': [72, 92]})

# Melt the DataFrame
df_melted = df.melt(id_vars=['year', 'artist', 'track'],
                    value_name='rank', var_name='week')

print(df_melted)

• Key point: Retains identifying variables (id_vars) while pivoting others into a “variable” and “value” column.

2. Splitting Strings and Extracting Values: str.split() and str.get()

• Manipulate string data: Divide strings based on a delimiter and extract specific parts.
• Example:

df_long['country'] = df_long.cd_country.str.split("_").str.get(0)

• Common use case: Extracting country codes from combined country-city columns.

3. Dropping Columns Efficiently: df.drop()

• Remove unwanted columns: Specify either the column name or its axis index.
• Example:

df_long = df_long.drop("cd_country", axis='columns')  # Equivalent to axis=1

4. Handling Null Values When Converting Data Types: pd.to_numeric()

• Convert columns to numeric types: Employ pd.to_numeric() for robustness against null values.
• Example:

df_long['count'] = pd.to_numeric(df_long['count'])  # Safer than df_long['count'].astype(int)

5. Pivoting Data for Multidimensional Analysis: df.pivot_table()

• Reshape data for aggregation and analysis: Create multidimensional tables for flexible exploration.
• Example:

df.pivot_table(index=['id', 'year', 'month', 'day'], columns='element', values='temp').reset_index()

• Common use case: Analyzing temperature data across multiple dimensions (id, time, elements).

Master these functions and reshape your data with confidence!