Source: https://www.w3resource.com/python-exercises/pandas/pandas_100_exercises_with_solutions.php

Exercise 1:

Create a DataFrame from a dictionary of lists.

Exercise 2:

Select the first 3 rows of a DataFrame.

Exercise 3:

Select the 'X' column from a DataFrame.

Exercise 4:

Filter rows based on a column condition.

Exercise 5:

Add a new column to an existing DataFrame.

Exercise 6:

Remove a column from a DataFrame.

Exercise 7:

Sort a DataFrame by a column.

Exercise 8:

Group a DataFrame by a column and calculate the mean of each group.

Exercise 9:

Replace missing values in a DataFrame.

Exercise 10:

Convert a column to datetime.

Exercise 11:

Create a DataFrame with specific column names.

Exercise 12:

Calculate the sum of values in each column.

Exercise 13:

Calculate the mean of values in each row.

Exercise 14:

Concatenate two DataFrames.

Exercise 15:

Merge two DataFrames on a key.

Exercise 16:

Create a pivot table from a DataFrame.

Exercise 17:

Reshape a DataFrame from long to wide format.

Exercise 18:

Calculate the correlation between columns in a DataFrame.

Exercise 19:

Iterate over rows in a DataFrame using iterrows().

Exercise 20:

Apply a function to each element in a DataFrame.

Exercise 21:

Create a DataFrame from a list of dictionaries.

Exercise 22:

Rename columns in a DataFrame.

Exercise 23:

Filter rows by multiple conditions.

Exercise 24:

Calculate the cumulative sum of a column.

Exercise 25:

Drop rows with missing values.

Exercise 26:

Replace values in a DataFrame based on a condition.

Exercise 27:

Create a DataFrame with a MultiIndex.

Exercise 28:

Calculate the rolling mean of a column.

Exercise 29:

Create a DataFrame from a list of tuples.

Exercise 30:

Add a row to a DataFrame.

Exercise 31:

Create a DataFrame with random values.

Exercise 32:

Calculate the rank of values in a DataFrame.

Exercise 33:

Change the data type of a column.

Exercise 34:

Filter rows based on string matching.

Exercise 35:

Create a DataFrame with specified row and column labels.

Exercise 36:

Transpose a DataFrame.

Exercise 37:

Set a column as the index of a DataFrame.

Exercise 38:

Reset the index of a DataFrame.

Exercise 39:

Add a prefix or suffix to column names.

Exercise 40:

Filter rows based on datetime index.

Exercise 41:

Create a DataFrame with duplicate rows and remove duplicates.

Exercise 42:

Create a DataFrame with hierarchical index.

Exercise 43:

Calculate the difference between consecutive rows in a DataFrame.

Exercise 44:

Create a DataFrame with hierarchical columns.

Exercise 45:

Filter rows based on the length of strings in a column.

Exercise 46:

Calculate the percentage change between rows in a DataFrame.

Exercise 47:

Create a DataFrame from a dictionary of Series.

Exercise 48:

Filter rows based on whether a column value is in a list.

Exercise 49:

Calculate the z-score of values in a DataFrame.

Exercise 50:

Create a DataFrame with random integers and calculate descriptive statistics.

Exercise 51:

Calculate the rank of values in each column of a DataFrame.

Exercise 52:

Filter rows based on multiple string conditions.

Exercise 53:

Create a DataFrame with random values and calculate the skewness.

Exercise 54:

Create a DataFrame and calculate the kurtosis.

Exercise 55:

Calculate the cumulative product of a column in a DataFrame.

Exercise 56:

Create a DataFrame and calculate the rolling standard deviation.

Exercise 57:

Create a DataFrame and calculate the expanding mean.

Exercise 58:

Create a DataFrame with random values and calculate the covariance matrix.

Exercise 59:

Create a DataFrame with random values and calculate the correlation matrix.

Exercise 60:

Create a DataFrame and calculate the rolling correlation between two columns.

Exercise 61:

Create a DataFrame and calculate the expanding variance.

Exercise 62:

Create a DataFrame with datetime index and resample by month.

Exercise 63:

Create a DataFrame and calculate the exponential moving average.

Exercise 64:

Create a DataFrame with random integers and calculate the mode.

Exercise 65:

Create a DataFrame and calculate the z-score of each column.

Exercise 66:

Create a DataFrame with random values and calculate the median.

Exercise 67:

Create a DataFrame and apply a custom function to each column.

Exercise 68:

Create a DataFrame with hierarchical index and calculate the mean for each group.

Exercise 69:

Create a DataFrame and calculate the percentage of missing values in each column.

Exercise 70:

Create a DataFrame and apply a custom function to each row.

Exercise 71:

Create a DataFrame with random values and calculate the quantiles.

Exercise 72:

Create a DataFrame and calculate the interquartile range (IQR).

Exercise 73:

Create a DataFrame with datetime index and calculate the rolling mean.

Exercise 74:

Create a DataFrame and calculate the cumulative maximum.

Exercise 75:

Create a DataFrame and calculate the cumulative minimum.

Exercise 76:

Create a DataFrame with random values and calculate the cumulative variance.

Exercise 77:

Create a DataFrame and apply a custom function to each element.

Exercise 78:

Create a DataFrame with random values and calculate the z-score for each element.

Exercise 79:

Create a DataFrame and calculate the cumulative sum for each group.

Exercise 80:

Create a DataFrame with random values and calculate the rank for each element.

Exercise 81:

Create a DataFrame and calculate the cumulative product for each group.

Exercise 82:

Create a DataFrame with random values and calculate the expanding sum.

Exercise 83:

Create a DataFrame and calculate the expanding minimum for each group.

Exercise 84:

Create a DataFrame with random values and calculate the expanding maximum for each group.

Exercise 85:

Create a DataFrame and calculate the expanding variance for each group.

Exercise 86:

Create a DataFrame with random values and calculate the expanding standard deviation.

Exercise 87:

Create a DataFrame and calculate the expanding covariance.

Exercise 88:

Create a DataFrame with random values and calculate the expanding correlation.

Exercise 89:

Create a DataFrame and calculate the expanding median.

Exercise 90:

Create a DataFrame with datetime index and calculate the expanding mean for each group.

Exercise 91:

Create a DataFrame with random values and calculate the rolling sum for each group.

Exercise 92:

Create a DataFrame and calculate the rolling mean for each group.

Exercise 93:

Create a DataFrame with random values and calculate the rolling standard deviation for each group.

Exercise 94:

Create a DataFrame and calculate the rolling variance for each group.

Exercise 95:

Create a DataFrame with random values and calculate the rolling correlation for each group.

Exercise 96:

Create a DataFrame and calculate the rolling covariance for each group.

Exercise 97:

Create a DataFrame with random values and calculate the rolling skewness for each group.

Exercise 98:

Create a DataFrame and calculate the rolling kurtosis for each group.

Exercise 99:

Create a DataFrame with random values and calculate the rolling median for each group.

Exercise 100:

Create a DataFrame and calculate the expanding sum for each group.