Fill In the Blanks For Python Pandas 1 Class 12 Informatics Practices

Fill In the Blanks For Python Pandas 1 Class 12 Informatics Practices

1. _____ is the most popular open-source Python library used for doing data analysis.
2. In order to work with Pandas in Python, you need to _____ library in your Python environment.
3. The two basic and universally popular data structures of Pandas are _____ and _____.
4. A _____ is a Pandas data structure that represents a one-dimensional array-like object of indexed data.
5. To create a series object, _____ method is used.
6. To create an empty series object, Series() method is used with _____.
7. Missing data in Pandas series and dataframes can be filled with a _____ value.
8. Dataframe has _____ indices.
9. Selecting a subset from a dataframe requires _____ function.
10. _____ function is used to read data from a CSV file in your dataframe.
11. _____ function saves the data of dataframe on a CSV file.
12. _____ is a popular data-science library of Python.
13. A _____ is a Pandas data structure that represents a 1 D array like object.
14. A _____ is a Pandas data structure that represents a 2 D array like object.
15. You can use numpy. _____ for missing data.
16. To specify datatype for a Series object, _____ argument is used.
17. The _____ function on Series object returns total elements in it including NaNs.
18. The _____ function on Series object returns only the count of non-NaN values in it.
19. Series is _____ mutable.
20. Series is not _____ mutable.
21. Dataframe is _____ mutable as well as _____ mutable.
22. To access values using row labels you can use DF. _____.
23. To access individual value, you can use DF. _____ using row/column index labels.
24. To access individual value, you can use DF. _____ using row/column integer position.
25. The rename() function requires _____ argument to make changes in the original dataframe.


Answer :-

1. Pandas
2. import pandas
3. Series, dataframe
4. Series
5. Series()
6. no parameter
7. NaN-Not a Number
8. two
9. loc
10. pandas.read_csv();
11. to csv()
12. Pandas
13. series
14. DataFrame
15. NaN
16. dtype
17. len()
18. count()
19. value
20. size
21. size, value
22. loc
23. at
24. iat
25. inplace