# Pandas and NumPy Exercises, Practice, Solution for Data Analysis

## Python Pandas Numpy [37 exercises with solution]

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**1.** Load a CSV file into a Pandas DataFrame.

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**2.** Create a DataFrame from a NumPy array with custom column names.

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**3.** Select rows from a DataFrame based on multiple conditions.

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**4.** Select the first and last 7 rows of a Pandas DataFrame.

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**5.** Filter rows based on a condition in a specific column in a Pandas DataFrame.

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**6.** Create a new column in a Pandas DataFrame based on the result of a NumPy operation.

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**7.** Merge two Pandas DataFrames based on a common column.

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**8.** Extract rows from a Pandas DataFrame where a specific column's values are in a given NumPy array.

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**9.** Perform element-wise addition of a NumPy array and a Pandas DataFrame column.

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**10.** Apply a NumPy function to a Pandas DataFrame column.

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**11.** Calculate the correlation matrix for a Pandas DataFrame.

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**12.** Calculate the cumulative sum of a NumPy array and store the results in a new Pandas DataFrame column.

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**13.** Group a Pandas DataFrame by a column and calculate the mean of another column.

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**14.** Reshape a Pandas DataFrame using the pivot_table function.

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**15.** Replace missing values in a Pandas DataFrame with the mean of the column.

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**16.** Create a histogram of a numerical column using NumPy and Matplotlib.

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**17.** Normalize a numerical column in a Pandas DataFrame.

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**18.** Remove duplicate rows from a Pandas DataFrame.

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**19.** Perform element-wise addition on two NumPy arrays.

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**20.** Calculate the dot product of two NumPy arrays.

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**21.** Find the index of the maximum and minimum value in a NumPy array.

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**22.** Reshape a 1D NumPy array into a 2D array.

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**23.** Slice and extract a portion of a NumPy array.

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**24.** Concatenate two NumPy arrays vertically.

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**25.** Perform matrix multiplication using NumPy.

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**26.** Calculate the cumulative sum of a NumPy array.

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**27.** Create a NumPy array with random values and find the unique values.

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**28.** Sort a Pandas DataFrame by values in a specific column.

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**29.** Apply a custom function to each element in a Pandas DataFrame.

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**30.** Rename columns in a Pandas DataFrame.

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**31.** Create a new DataFrame by transposing an existing one.

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**32.** Merge two DataFrames based on multiple columns.

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**33.** Aggregate data in a DataFrame by multiple functions.

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**34.** Extract the date and time components from a DateTime column.

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**35.** Resample time-series data in a DataFrame.

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**36.** Perform a rolling calculation on a numerical column in a DataFrame.

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**37.** Perform a cross-tabulation between two columns in a DataFrame.

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