Python Pandas Interview Questions and Answers

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100 Python Pandas Interview Questions and Answers

  1. What is Pandas?

    • Answer: Pandas is a powerful and versatile Python library used for data manipulation and analysis. It provides high-performance, easy-to-use data structures and data analysis tools.

  2. What are the key data structures in Pandas?

    • Answer: The two primary data structures in Pandas are Series (1-dimensional) and DataFrame (2-dimensional).

  3. How do you create a Pandas Series?

    • Answer: You can create a Pandas Series from various sources like lists, dictionaries, NumPy arrays, etc. For example: `pd.Series([1, 2, 3, 4])` or `pd.Series({'a': 1, 'b': 2})`

  4. How do you create a Pandas DataFrame?

    • Answer: DataFrames can be created from lists of lists, dictionaries, NumPy arrays, or CSV files. Examples include: `pd.DataFrame([[1, 2], [3, 4]])` or `pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})`

  5. Explain the difference between loc and iloc in Pandas.

    • Answer: `loc` uses labels for indexing (row and column names), while `iloc` uses integer positions.

  6. How do you read a CSV file into a Pandas DataFrame?

    • Answer: Use `pd.read_csv('file.csv')`

  7. How do you write a Pandas DataFrame to a CSV file?

    • Answer: Use `df.to_csv('file.csv')`

  8. How do you select a specific column from a DataFrame?

    • Answer: `df['column_name']` or `df.column_name`

  9. How do you select multiple columns from a DataFrame?

    • Answer: `df[['column1', 'column2']]`

  10. How do you select rows based on a condition?

    • Answer: `df[df['column_name'] > 10]`

  11. How do you filter a DataFrame based on multiple conditions?

    • Answer: Use boolean operators like `&` (and) and `|` (or). Example: `df[(df['column1'] > 10) & (df['column2'] < 5)]`

  12. What is the purpose of the `groupby()` method?

    • Answer: `groupby()` groups rows based on the values in one or more columns, allowing for aggregate calculations on each group.

  13. How do you handle missing data in Pandas?

    • Answer: Use methods like `fillna()` to replace missing values (NaN) with a specific value or using imputation techniques. `dropna()` removes rows or columns with missing values.

  14. What are some common aggregation functions in Pandas?

    • Answer: `mean()`, `sum()`, `count()`, `min()`, `max()`, `median()`, `std()`, `var()`

  15. Explain the use of the `apply()` method.

    • Answer: `apply()` applies a function to each element or row/column of a Series or DataFrame.

  16. How do you merge two DataFrames?

    • Answer: Use the `merge()` method, specifying the join type (inner, outer, left, right) and the columns to join on.

  17. What is the difference between `concat()` and `append()`?

    • Answer: `concat()` is more general and can join multiple DataFrames or Series along different axes. `append()` is a specific case of `concat()` for adding a single DataFrame or Series at the end.

  18. How do you pivot a DataFrame?

    • Answer: Use the `pivot()` method to reshape the DataFrame from a "long" format to a "wide" format.

  19. How do you unpivot a DataFrame?

    • Answer: Use the `melt()` method to reshape the DataFrame from a "wide" format to a "long" format.

  20. What is data cleaning in Pandas?

    • Answer: Data cleaning involves handling missing values, removing duplicates, correcting inconsistencies, and transforming data into a usable format for analysis.

  21. How do you handle duplicate rows in a DataFrame?

    • Answer: Use `df.duplicated()` to identify duplicates and `df.drop_duplicates()` to remove them.

  22. How do you change the data type of a column?

    • Answer: Use `df['column_name'] = df['column_name'].astype('new_data_type')`

  23. How do you rename columns in a DataFrame?

    • Answer: Use `df.rename(columns={'old_name': 'new_name'})`

  24. How do you sort a DataFrame?

    • Answer: Use `df.sort_values(by=['column_name'], ascending=True/False)`

  25. How do you add a new column to a DataFrame?

    • Answer: `df['new_column'] = values`

  26. How do you delete a column from a DataFrame?

    • Answer: `del df['column_name']` or `df.drop(columns=['column_name'])`

  27. How do you find the unique values in a column?

    • Answer: `df['column_name'].unique()`

  28. How do you count the occurrences of each unique value in a column?

    • Answer: `df['column_name'].value_counts()`

  29. What is a rolling window in Pandas?

    • Answer: A rolling window allows you to apply a function to a moving window of data, useful for time series analysis.

  30. How do you perform a rolling mean calculation?

    • Answer: `df['column_name'].rolling(window=n).mean()` where n is the window size.

  31. What is resampling in Pandas?

    • Answer: Resampling changes the frequency of time series data (e.g., from daily to monthly).

  32. How do you resample time series data?

    • Answer: `df.resample('M').mean()` (for monthly mean)

  33. What is the purpose of the `map()` method?

    • Answer: `map()` applies a function to each element of a Series, often used for data transformation.

  34. How do you create a scatter plot from a DataFrame?

    • Answer: Use Matplotlib: `df.plot.scatter(x='column1', y='column2')`

  35. How do you create a histogram from a DataFrame?

    • Answer: Use Matplotlib: `df['column_name'].plot.hist()`

  36. How do you handle string data in Pandas?

    • Answer: Use string methods like `.str.lower()`, `.str.upper()`, `.str.replace()`, `.str.contains()`

  37. What is vectorization in Pandas?

    • Answer: Vectorization allows for performing operations on entire arrays or Series at once, rather than element by element, resulting in significant performance improvements.

  38. Explain the concept of indexing in Pandas.

    • Answer: Indexing is how you access specific elements or subsets of data within a Series or DataFrame. Pandas supports label-based and integer-based indexing.

  39. What is a MultiIndex in Pandas?

    • Answer: A MultiIndex is a hierarchical index, allowing for multiple levels of indexing.

  40. How do you handle categorical data in Pandas?

    • Answer: Use the `Categorical` data type to efficiently represent and work with categorical variables.

  41. What is a pivot table in Pandas?

    • Answer: A pivot table is a data summarization tool that allows for aggregating data based on different groupings.

  42. How do you create a pivot table in Pandas?

    • Answer: Use the `pivot_table()` method, specifying the index, columns, values, and aggregation function.

  43. How do you perform time series analysis in Pandas?

    • Answer: Pandas provides tools for working with datetime data, including resampling, rolling windows, and time-based indexing.

  44. Explain the use of `datetime` objects in Pandas.

    • Answer: `datetime` objects are used to represent dates and times, allowing for powerful time-series analysis.

  45. How do you convert a string column to datetime?

    • Answer: Use `pd.to_datetime()`

  46. What are some common ways to improve the performance of Pandas operations?

    • Answer: Vectorization, using optimized data types (like Categorical), and using appropriate data structures.

  47. How do you work with large datasets in Pandas?

    • Answer: Techniques like chunking (reading data in smaller parts), Dask (for parallel processing), and optimized data structures are essential.

  48. What is the difference between a Series and a DataFrame?

    • Answer: A Series is one-dimensional, like a column, while a DataFrame is two-dimensional, like a table.

  49. What is the purpose of the `head()` and `tail()` methods?

    • Answer: `head()` displays the first few rows, and `tail()` displays the last few rows of a DataFrame.

  50. How do you calculate the correlation between two columns?

    • Answer: `df['column1'].corr(df['column2'])`

  51. How do you calculate the covariance between two columns?

    • Answer: `df['column1'].cov(df['column2'])`

  52. What is data wrangling?

    • Answer: Data wrangling is the process of cleaning, transforming, and preparing data for analysis.

  53. What are some common data wrangling tasks?

    • Answer: Handling missing values, removing duplicates, transforming data types, and data normalization.

  54. How do you use lambda functions with Pandas?

    • Answer: Lambda functions are useful for applying simple, anonymous functions within Pandas, often with `apply()`.

  55. What are some best practices for using Pandas?

    • Answer: Use descriptive column names, handle missing data appropriately, optimize for performance, and write clean and well-documented code.

  56. How do you create a box plot from a DataFrame?

    • Answer: Use Matplotlib or Seaborn: `df.plot.box()` or `sns.boxplot(data=df)`

  57. How do you handle outliers in a DataFrame?

    • Answer: Methods include removing outliers, transforming the data (e.g., log transformation), or using robust statistical methods.

  58. What is the difference between `unique()` and `nunique()`?

    • Answer: `unique()` returns the unique values, while `nunique()` returns the number of unique values.

  59. How do you find the index of a specific row?

    • Answer: Use `df[df['column_name'] == value].index`

  60. How do you create a new DataFrame from an existing one?

    • Answer: Use slicing, filtering, or the `copy()` method to create a new DataFrame.

  61. Explain the concept of chained indexing in Pandas.

    • Answer: Chained indexing (e.g., `df['col1']['col2']`) can be slower and less efficient than other methods. It's often better to use `.loc` or `.iloc`.

  62. How do you handle dates with different formats in a DataFrame?

    • Answer: Use `pd.to_datetime()` with the `format` argument to specify the date format.

  63. What is the purpose of the `clip()` method?

    • Answer: `clip()` limits values within a specified range.

  64. How do you calculate the cumulative sum of a column?

    • Answer: `df['column_name'].cumsum()`

  65. How do you perform string manipulations on multiple columns?

    • Answer: Use the `.apply()` method with a function that operates on multiple columns.

  66. What is the role of the `axis` parameter in many Pandas functions?

    • Answer: The `axis` parameter specifies whether to operate along rows (axis=0) or columns (axis=1).

  67. How do you check the data types of each column in a DataFrame?

    • Answer: `df.dtypes`

  68. How do you create a crosstab in Pandas?

    • Answer: Use the `pd.crosstab()` function to create a cross-tabulation of categorical variables.

  69. How do you find the number of rows and columns in a DataFrame?

    • Answer: `df.shape`

  70. What is the `info()` method used for?

    • Answer: The `info()` method provides a concise summary of the DataFrame, including data types, non-null values, and memory usage.

  71. How do you reset the index of a DataFrame?

    • Answer: `df.reset_index()`

  72. How do you set a specific column as the index?

    • Answer: `df.set_index('column_name')`

  73. How do you deal with different date/time formats in a single column?

    • Answer: Use error handling within `pd.to_datetime()` or pre-process the data to standardize the formats.

  74. What is the purpose of the `cut()` function?

    • Answer: `cut()` divides a continuous variable into discrete intervals (bins).

  75. How do you use the `qcut()` function?

    • Answer: `qcut()` divides a continuous variable into quantiles (equal-sized groups).

  76. How to handle large CSV files that don't fit into memory?

    • Answer: Use the `chunksize` parameter in `pd.read_csv()` to read the file in smaller chunks.

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