Interview Bite

1. What is Pandas?

Pandas is a powerful Python library designed for data manipulation and analysis. It provides high-performance data structures and data analysis tools that make working with structured data efficient and easy.

2. What are the main data structures in Pandas?

The main data structures in Pandas are:

• Series: A one-dimensional labeled array, similar to a NumPy array but with additional features like labels (indices) for efficient data access and selection.
• DataFrame: A two-dimensional labeled data structure with rows and columns, resembling a spreadsheet or SQL table.

3. How do you create a Series and DataFrame in Pandas?

import pandas as pd

# Create a Series
series = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])

# Create a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)

4. How do you access elements in a Series and DataFrame?

# Accessing elements in a Series
print(series['b'])  # Output: 2

# Accessing elements in a DataFrame
print(df['Name'][0])  # Output: Alice

5. How do you select rows and columns in a DataFrame?

Selecting rows: df[0:2] (selects rows 0 to 1), df.loc[0:2] (selects rows by label), df.iloc[0:2] (selects rows by integer position)

Selecting columns: df['Name'] (selects the ‘Name’ column), df[['Name', 'Age']] (selects multiple columns)

6. How do you filter data in a DataFrame based on conditions?

filtered_df = df[df['Age'] > 30]

7. How do you handle missing values in Pandas?

Check for missing values: df.isnull()

Fill missing values: df.fillna(0, inplace=True)

Remove missing values: df.dropna()

8. How do you group data and calculate summary statistics in Pandas?

grouped_df = df.groupby('Name').mean()

9. How do you merge or join DataFrames in Pandas?

# Merge DataFrames based on a common column
merged_df = pd.merge(df1, df2, on='key')

10. How do you create basic plots using Pandas?

# Create a bar plot
df['Age'].plot(kind='bar')

11. What is indexing in Pandas?

Indexing refers to assigning labels (indices) to rows and columns of a DataFrame for efficient data access and selection.

12. What is resampling in Pandas?

Resampling is the process of changing the frequency of time series data. For example, resampling hourly data to daily or weekly.

13. What is pivoting in Pandas?

Pivoting is rearranging the structure of a DataFrame, often used for data aggregation. It’s similar to the pivot table feature in Excel.

14. How do you handle categorical data in Pandas?

Use the pd.Categorical data type to represent categorical variables efficiently. This data type provides methods for converting between categories and codes, and for frequency analysis.

15. What is vectorization in Pandas?

Vectorization is the process of performing operations on entire arrays or DataFrames at once, rather than element-by-element. This can significantly improve performance, especially for large datasets.

16. How can Pandas be used for data cleaning and preprocessing?

Pandas can be used for data cleaning and preprocessing by handling missing values, removing duplicates, normalizing data, and more.

17. How can Pandas be used for data analysis and exploration?

Pandas can be used for data analysis and exploration by calculating summary statistics, visualizing data, identifying patterns, and more.

18. How can Pandas be used for time series analysis?

Pandas can be used for time series analysis by handling time-based data, forecasting, trend analysis, and more.

19. How can Pandas be used for machine learning?

Pandas can be used for machine learning by preparing data, creating features, splitting data, and more.

20. What are some common performance optimization techniques in Pandas?

Some common performance optimization techniques in Pandas include using appropriate data types, avoiding unnecessary copies, leveraging vectorized operations, using optimized functions, and using Dask for large datasets.