Pandas Quiz - Learn DA-DS Course in Hindi

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Question 1 of 25:

1: Which of the following is a core data structure in Pandas?

DataFrame

List

Tuple

Set

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Question 2 of 25:

2: How do you read a CSV file into a Pandas DataFrame?

pd.read_csv()

pd.load_csv()

pd.import_csv()

pd.DataFrame.read()

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Question 3 of 25:

3: Which function is used to view the first 5 rows of a Pandas DataFrame?

df.show()

df.first()

df.head()

df.tail()

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Question 4 of 25:

4: How can you get the shape (rows, columns) of a DataFrame df in Pandas?

df.size

df.itemsize

df.info()

df.shape

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Question 5 of 25:

5: What does df.describe() return in Pandas?

A summary of statistics for numerical columns

A list of column names

The number of missing values in each column

A summary of the data types of the columns

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Question 6 of 25:

6: How do you rename columns in a Pandas DataFrame?

df.columns.rename({'old_name': 'new_name'})

df.rename(columns={'old_name': 'new_name'})

df.rename_column({'old_name': 'new_name'})

df.rename_columns({'old_name': 'new_name'})

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Question 7 of 25:

7: What function is used to check for missing values in a DataFrame?

df.isnull()

df.check_missing()

df.missing()

df.find_null()

8 / 25

Question 8 of 25:

8: How can you drop a column in Pandas DataFrame df?

df.remove('column_name')

df.drop('column_name')

df.drop(columns='column_name')

df.delete_column('column_name')

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Question 9 of 25:

9: How can you filter rows in a DataFrame based on a condition?

df[condition]

df.filter(condition)

df.where(condition)

df.select(condition)

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Question 10 of 25:

10: What function is used to concatenate two DataFrames vertically in Pandas?

df.merge()

df.append()

df.combine()

pd.concat()

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Question 11 of 25:

11 : How can you set a column as the index in a Pandas DataFrame?

df.set_index_by('column_name')

df.index('column_name')

df.set_index('column_name')

df.set_column_as_index('column_name')

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Question 12 of 25:

12 : What method is used to group data in a Pandas DataFrame?

df.aggregate()

df.groupby()

df.sortby()

df.partition()

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Question 13 of 25:

13 : Which of the following is the correct way to reset the index of a Pandas DataFrame df?

df.reset()

df.index.reset()

df.set_index(None)

df.reset_index()

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Question 14 of 25:

14 : What is the output of the following code: df['column_name'].unique()?

The total number of unique values in the column

A list of unique values in the column

The count of non-null values in the column

The first unique value in the column

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Question 15 of 25:

15 : Which of the following is used to combine two DataFrames based on common columns or indices?

df.concatenate()

df.join()

df.union()

df.merge()

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Question 16 of 25:

16 : How do you select a specific column from a DataFrame df?

df.column name

df.select('column_name')

df['column_name']

df.get('column_name')

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Question 17 of 25:

17 : What is the function to check the data types of all columns in a DataFrame?

df.data_types()

df.dtypes

df.info()

df.check_dtypes()

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Question 18 of 25:

18 : How can you sort a DataFrame by a specific column?

df.sort('column_name')

df.order_by('column_name')

df.sort_column('column_name')

df.sort_values('column_name')

19 / 25

Question 19 of 25:

19 : What is the purpose of df.apply() in Pandas?

To apply a function along the rows or columns of the DataFrame

To transform data in the DataFrame

To apply conditional formatting to the DataFrame

To reset the index of the DataFrame

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Question 20 of 25:

20 : Which of the following functions will give you the number of non-null values in each column?

df.sum()

df.count()

df.value_counts()

df.isnull().sum()

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Question 21 of 25:

21 : What is the function used to merge two DataFrames based on their index?

df.join()

df.concatenate()

df.merge()

df.append()

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Question 22 of 25:

22 : How do you get the first row of a DataFrame df?

df.first()

df.head(1)

df.iloc[0]

df.row(1)

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Question 23 of 25:

23 : Which of the following functions is used to create a new DataFrame by removing duplicate rows?

df.unique()

df.remove_duplicates()

df.delete_duplicates()

df.drop_duplicates()

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Question 24 of 25:

24 : How can you replace all occurrences of a value in a DataFrame?

df.replace(value, new_value)

df.transform(value, new_value)

df.change(value, new_value)

df.substitute(value, new_value)

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Question 25 of 25:

25 : Which of the following is used to find the sum of all values in a particular column?

df.sum()

df.total()

df.add()

df.total_sum()

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