Day 12 of Learning Python for Data Science – Pandas

Welcome to Day 12 of Learning Python for Data Science. Today, we’ll dive into Pandas, one of the most essential libraries for data manipulation and analysis in Python. Pandas provides powerful, easy-to-use data structures like Series and DataFrames that simplify handling structured data. In this article, we’ll explore how to load, inspect, clean, and transform data using Pandas — key skills for any data science project. Whether you’re working with CSV files, handling missing data, or performing group operations, Pandas offers efficient tools to streamline the entire process.

Day 10 Of Learning Python for Data Science – NumPy Array In Python

Creating & Viewing DataFrames

Creating a DataFrame

import pandas as pd
import numpy as np

data = {'Name': ['Sneha', 'Alice', 'Bob', 'Peter'],
        'Age': [27, 28, np.nan, 20],
        'City': ['Hyd', 'Guj', 'Luk', 'Blg']}

df = pd.DataFrame(data)
print(df)

Viewing Data

print(df.head(2))  # First 2 rows
print(df.tail(2))  # Last 2 rows
print(df.info())   # Summary of DataFrame
print(df.shape)    # Number of rows & columns
print(df.columns)  # List of column names
print(df.index)    # Row labels
print(df.describe())  # Statistical summary

Indexing & Selection

print(df['Name'])         # Select single column
print(df[['Name', 'City']])  # Select multiple columns
print(df.loc[1, 'Name'])  # Label-based indexing
print(df.iloc[0:2, 0:3])  # Position-based indexing
print(df.at[1, 'Name'])   # Fast access by label
print(df.iat[1, 1])       # Fast access by index position

Data Manipulation

Sorting & Dropping

df.sort_values(by='Age', ascending=False, inplace=True)
df.drop(columns=['City'], inplace=True)

Renaming & Indexing

df.rename(columns={'Age': 'Years'}, inplace=True)
df.set_index('Name', inplace=True)
df.reset_index(inplace=True)

Handling Missing Values

df.fillna(df['Age'].mean(), inplace=True)
df.dropna(inplace=True)

Changing Data Types

df['Age'] = df['Age'].astype(int)

Aggregation & Grouping

print(df.groupby('City').mean())
print(df.agg({'Age': 'sum', 'City': 'count'}))
print(df.transform(lambda x: x - x.mean()))

Data Joining & Merging

df1 = pd.DataFrame({'ID': [1, 2], 'Name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'ID': [1, 2], 'City': ['NY', 'LA']})
merged_df = pd.merge(df1, df2, on='ID')
print(merged_df)

concat_df = pd.concat([df1, df2], axis=1)
print(concat_df)

String Operations

df['Name'] = df['Name'].str.upper()
print(df['Name'].str.contains('ALICE'))
df['Name'] = df['Name'].str.replace('SNEHA', 'SARA')

Handling Time Series Data

df['Date'] = pd.to_datetime(df['Date'])
df.set_index('Date', inplace=True)
print(df.resample('M').sum())
print(df.shift(1))

Numerical Operations

print(df.sum())
print(df.mean())
print(df.median())
print(df.min())
print(df.max())
print(df.std())
print(df.cumsum())
print(df.cumprod())

Pivoting & Melting

pivot_df = df.pivot(index='City', columns='Name', values='Age')
print(pivot_df)

melted_df = df.melt(id_vars=['City'], value_vars=['Age'])
print(melted_df)

Visualization

df.plot(kind='line')
df.hist()

Practice Questions

Beginner:

1. Create a DataFrame from a dictionary.
2. Display the first 5 rows of a DataFrame.
3. Retrieve a single column as a Series.
4. Find the number of missing values in each column.
5. Sort the DataFrame by a specific column.
6. Select rows where a column’s value is greater than 50.
7. Rename a column.
8. Drop a column from the DataFrame.
9. Fill missing values with the mean.
10. Add a new column to the DataFrame.

Intermediate:

11. Select a subset of rows and columns using loc and iloc.
12. Convert a column to a different data type.
13. Find the average of all numerical columns.
14. Count the number of unique values in a column.
15. Use groupby() to find the mean of a column by category.
16. Merge two DataFrames using merge().
17. Find the most frequent value in a column.
18. Compute cumulative sum of a column.
19. Apply a custom function to transform a column.
20. Use pivot_table() to summarize data.

Advanced:

21. Reshape a DataFrame using melt().
22. Create a time series and resample it to monthly data.
23. Use applymap() to apply a function to every element in the DataFrame.
24. Use map() to apply a function to a Series.
25. Create a scatter plot using Pandas.
26. Compute rolling averages using rolling().
27. Rank values in a column.
28. Filter rows based on multiple conditions.
29. Use .xs() to slice data from a multi-index DataFrame.
30. Implement a lambda function inside apply() for custom transformations.

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