Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Pandas is used for data manipulation, analysis and cleaning.

**What are Data Frames and Series?**

**Dataframe** is a two dimensional, size mutable, potentially heterogeneous tabular data.

It contains rows and columns, arithmetic operations can be applied on both rows and columns.

**Series** is a one dimensional label array capable of holding data of any type. It can be integer, float, string, python objects etc. Panda series is nothing but a column in an excel sheet.

s = pd.Series([1,2,3,4,56,np.nan,7,8,90])

print(s)

**How to create a dataframe by passing a numpy array?**

- d= pd.date_range(‘20200809’,periods=15)
- print(d)
- df = pd.DataFrame(np.random.randn(15,4), index= d, columns = [‘A’,’B’,’C’,’D’])
- print(df)

pandas-series pandas pandas-in-python pandas-dataframe python

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:- ### Pandas Series Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float...

In this video, we will be learning about the Pandas DataFrame and Series objects. This video is sponsored by Brilliant. Go to https://brilliant.org/cms to si...

Pandas Series is a one-dimensional data structure designed as a labeled array capable of holding any type of data.

Handling NaN in Series is Mandatory to learn to start with handling the Missing Data in field of Data Analytics .. Let’s explore the same…

In this video, we will be learning how to add and remove our rows and columns. This video is sponsored by Brilliant. Go to https://brilliant.org/cms to sign ...