#### Python Pandas (Series) Important Questions Answer

**Exam Special Python Pandas Assignment**

**Que 1. What is the significance of Pandas library?**

**Answer**: Pandas is a python module that makes data science or data analysis easy and effective. This is the famous python package for data science that offers powerful and flexible data structures which make data analysis and manipulation easy. Pandas make data importing and data analyzing easier.

**Que 2. Name the three well-established Python libraries for scientific and analytical use.**

**Answer**: NumPy, Pandas, and Matplotlib

**Que 3. NumPy, Pandas,** and Matplotlib libraries allow us to _____, ______,** and ______ data easily and efficiently.**

**Answer**: manipulate, transform, and visualise

**Que 4. NumPy stands for _______.**

**Answer**: Numerical Python

**Que 5. What is NumPy?**

**Answer**: Numerical Python is a package that can

be used for numerical data analysis and scientific computing.

- NumPy uses a multidimensional array object and has functions and tools for working with these arrays.
- Elements of an array stay together in memory, hence, they can be quickly accessed.

**Que 6. PANDAS stands for _______.**

**Answer**: PANel DAta System

**Que 7. What is PANDAS **?

**Answer**: PANDAS is a high-level data manipulation

tool used for analysing data.

- It is very easy to import and export data using the Pandas library which has a very rich set of functions.
- It is built on packages like NumPy and Matplotlib and gives us a single, convenient place to do most of our data analysis and visualisation work.
- Pandas have three important data structures, namely â€“ Series, DataFrame, and Panel to make the process of analysing data organised, effective, and efficient.

**Que 8. Name the important data structures of Pythonâ€™s Pandas library.**

**Answer**: Series, Dataframes and Panel

**Que 9. Which library in Python is used for plotting graphs and visualization**?

**Answer**: Matplotlib

**Que 10. What is Matplotlib?**

**Answer**: The Matplotlib library in Python is used for plotting graphs and visualisation.

- Using Matplotlib, with just a few lines of code we can generate publication-quality plots, histograms, bar charts, scatterplots, etc.
- It is also built on Numpy, and is designed to work well with Numpy and Pandas.

**Que 11. Differentiate between Pandas and NumPy?**

**Answer**: Differences between Pandas and Numpy are:

- A Numpy array requires homogeneous data, while a Pandas DataFrame can have different data types (float, int, string, datetime, etc.).
- Pandas have a simpler interface for operations like file loading, plotting, selection, joining, GROUP

BY, which come very handy in data-processing applications. - Pandas DataFrames (with column names) make it very easy to keep track of data.
- Pandas are used when data is in Tabular Format, whereas Numpy is used for numeric array-based

data manipulation.

**Que 12. Write the python command to install pandas.**

**Answer**: pip install pandas

**Que 14. What is data structure? Write commonly used data structures in pandas.**

**Answer**: A data structure is a collection of data values and operations that can be applied to that data.

It enables efficient storage, retrieval, and modification of the data.

Two commonly used data structures in Pandas are:

â€¢ Series

â€¢ DataFrame

**Que 15. What is Series?**

**Answer**: Series is a one-dimensional array containing a sequence of values of any data type like int, float, list, string, etc, which by default have numeric data labels starting from zero.

- The data label associated with a particular value is called its index.
- We can also assign values of other data types as index.
- Pandas Series as a column in a spreadsheet.

**Que 16. How is a Series object different from and similar to ndarrays? Give some examples.**

**Answer**: NumPy arrays or ndarrays are also similar to Series objects. But there are some differences:

- NumPy allows vectorized operation between two same shape ndarrays while In Series object allows vectorized operation between two different shape objects. In this case, NaN is returned for non-matching indexes.
- In ndarrays, indexes are always positive, starting from 0 while In Series objects can have any type of index, including numbers, letters, and labels. Strings, etc. In the case of numbers, starting with zero is unnecessary.

**Que 17. Write the command to import a Pandas library.**

**Answer**: import pandas

OR

import pandas as pd # importing pandas with alias pd

**Que 18. Which Pandas method is used to create a Series object?**

**Answer**: Series() method.

**Que 19. Which types of data can be used while creating a Series object in Pandas?**

**Answer**: (a) Python Sequence – List, Tuples, String

(b) Python Dictionary

(c) NumPy array – ndarray

(d) A scalar value

Example:

**Que 20. Write a command to create a Series object from a list of values.**

**Answer**: A series can be created by using a list of numbers as:

```
import pandas as pd #import Pandas with alias pd
series1 = pd.Series([10,20,30]) #create a Series
print(series1) #Display the series
```

**Output:**