{"id":58830,"date":"2024-04-16T01:26:25","date_gmt":"2024-04-16T01:26:25","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=58830"},"modified":"2024-04-16T01:26:25","modified_gmt":"2024-04-16T01:26:25","slug":"in-pandas-what-method-is-used-to-calculate-summary-statistics-for-numerical-columns-in-a-dataframe","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/in-pandas-what-method-is-used-to-calculate-summary-statistics-for-numerical-columns-in-a-dataframe\/","title":{"rendered":"In Pandas, what method is used to calculate summary statistics for numerical columns in a DataFrame?"},"content":{"rendered":"

[amp_mcq option1=”groupby()” option2=”value_counts()” option3=”describe()” option4=”reshape()” correct=”option3″]<\/p>\n

The correct answer is C. describe().<\/p>\n

The describe() method returns a DataFrame containing basic statistics for each numeric column in the DataFrame. The statistics include the count, mean, standard deviation, minimum, maximum, and 25th and 75th percentiles.<\/p>\n

The groupby() method groups the DataFrame by a specified column or columns and then calculates summary statistics for each group.<\/p>\n

The value_counts() method counts the number of times each unique value appears in a column.<\/p>\n

The reshape() method changes the shape of a DataFrame by reshaping the columns or rows.<\/p>\n

Here is an example of how to use the describe() method:<\/p>\n

“`
\nimport pandas as pd<\/p>\n

df = pd.DataFrame({‘A’: [1, 2, 3, 4, 5], ‘B’: [6, 7, 8, 9, 10]})<\/p>\n

df.describe()<\/p>\n

\n
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