Python Pandas for Beginners – A Complete Guide (Part 1)
Tags: pythonpandas, pythontutorials, python, pythonforbeginners
Published: 2019-11-02 03:14:55

---
title: Python Pandas for Beginners – A Complete Guide (Part 1)
published: true
date: 2019-10-17 08:50:42 UTC
tags: Python Pandas,Python Tutorials,python,python for beginners
canonical_url: https://pythongeeks.net/python-tutorials/python-pandas-for-beginners-a-complete-guide/
---

## Python Pandas for Beginners - A Complete Guide (Part 1)

 

This post series, _Python Pandas for Beginner, will be the best starting point to learn pandas library for the beginner. You will learn some of the most important pandas features such as exploring, cleaning, transforming, visualizing data._

**_Pandas_** is an open-source library in Python. It is the most popular Python library that is used for data analysis today. The powerful machine learning and visualization tools, it provides you the high-performance tool to analyze a big data set.

In this post, we will go over the essential information about pandas, from installation to advantage. You should make yourself a cup of coffee, take your favorite biscuit. After that, enjoy and read this article slowly. Feel free to stop and resume later, don't overwhelm yourself with a lot of info in a short time. Just follow step by step carefully, the pandas will come to you.

![Python Pandas for Beginners](https://i0.wp.com/pythongeeks.net/wp-content/uploads/2019/10/144074.jpg?resize=640%2C360&ssl=1)<figcaption>Hello Pandas <br>(Panda - Source: <a href="https://wallpaperplay.com">Wallpaper Play</a>)</figcaption>

## What is Pandas?

 

**_Pandas_** is a library for analytics, data processing, and data science. It's a huge open-source project with 1,500+ contributors. Here is the link of project [Pandas on GitHub](https://github.com/pandas-dev/pandas)

## Installations

 

The easiest way to install Pandas is by using Anaconda distribution. You haven't installed Anaconda yet, read our post for Anaconda installation guide.

If you don't want to install Anaconda, you can install it via pip.

```
pip install pandas
```

 

## Data Structure of Pandas

 

The two primary data structures of Pandas are **`Series`** and **`DataFrame`**. A `Series` is simply a column when we join multiple series (columns), so we have a `DataFrame`.

![Python Pandas Data Structure](https://i1.wp.com/pythongeeks.net/wp-content/uploads/2019/10/pandas_data_structure.png?w=640&ssl=1)<figcaption>Series and DataFrame in Pandas</figcaption>

 

## Creating Your Series And DataFrame

 

### Getting Start With Series

 

Firstly, creating a series data by passing a list of values. Pandas will count index from 0 by default.

```python
import numpy as np
import pandas as pd

data_series = pd.Series([1, 9, 3, np.nan, 8])
print(data_series)

""" Output:
0    1.0
1    9.0
2    3.0
3    NaN
4    8.0
dtype: float64
"""
```

 

### Create DataFrame In The Easiest Way

 

To create a DataFrame, there are many ways in Python. However, the easiest way is to create a `dict`. After that, pass the dictionary data to the DataFrame constructor and it will do the job.

```python
import pandas as pd

data = {
    'Paris': [3, 2, 0, 1], 
    'Berlin': [0, 3, 7, 2]
}

purchases = pd.DataFrame(data)
print(purchases)

""" Output:
     Paris    Berlin
0      3         0
1      2         3
2      0         7
3      1         2
"""
```

 

### Create DataFrame With Numpy

 

Passing the Numpy array, datetime data as index and column labels to DataFrame constructor:

```python
import numpy as np
import pandas as pd

dates = pd.date_range('20191001', periods=6)
dataframe = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
print(dataframe)

""" Output:
                 A         B         C         D
2019-10-01   0.304466 -0.699206 -2.090317  1.564566
2019-10-02  -0.876682  0.876720  1.275542 -0.757827
2019-10-03   0.029740 -1.282535 -0.420332 -1.176261
2019-10-04  -0.153740 -0.087788  1.314169 -1.835564
2019-10-05   0.301839  0.036301  0.138372  1.755769
2019-10-06   1.546020 -0.148291  0.781045 -1.789371
"""
```

In the example, we can see that `index` will represent row labels. In other way, `column` parameter is using for column labels.

 

## References

 

We have used below documents for the reference while creating the series. If you love to use Pandas, may be you should read it.

- [Pandas Official Document](https://pandas.pydata.org/pandas-docs/stable/index.html)

 

## Summary of Part 1

 

Through the first part of the series, Python Pandas for Beginners, you basically understand what is pandas and how to install it via pip or Anaconda. Furthermore, you can create your data Series or DataFrame.

In [part 2](https://pythongeeks.net/python-tutorials/python-pandas-for-beginners-a-complete-guide-part-2/), you will learn how to read the pandas data from JSON file, and some important operations of pandas.

See you in the next article, if you like this series, please share it for other Python Geeks. Leave a comment for us to help us improve in the next post.

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