To save your plot to a file (either Seaborn or Matplotlib) run this,
```python
#will save plot to filename.png file
plt.savefig('filename.png')
```

Saving a Plot to a File

https://www.geeksforgeeks.org/difference-between-matplotlib-vs-seaborn/

Geeks for Geeks Matplotlib vs Seaborn

Matplotlib is a Python library used for data visualization. It also uses other Python libraries specifically Numpy and Pandas as well. It is good for basic graphs. It is also similar to MATLAB. For more information, check out the full Matplotlib documentation.

Matplotlib

Seaborn is a Python library used for data visualization that is built off of Matplotlib and also uses other Python libraries such as Pandas and Numpy. It has a lot more themes than Matplotlib and works well as an extension of Matplotlib. The Seaborn syntax is simpler than Matplotlib's and has greater functionality as a whole. For more information, check out the full Seaborn documentation.

Seaborn

Pandas uses the plot() to create diagrams but Python Pyplot to actually display the digram.

To get started :
- import pandas as pd
- import matplotlib.pyplot as plt
- import seaborn as sns

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Pandas is a Python library for data manipulation and analysis.
Example import: `import pandas as pd`

Pandas library in Python

Pandas Documentation

The key data structure pandas supports, which is basically like a spreadsheet table with rows and named columns.

It is also called a 2 dimensional data structure

Pandas Dataframe

What does the following code do?

The Pandas Library allows the user to analyze big data and make an educated conclusion on statistical data. (i.e data sets)

Pandas - What is it Useful?

If Python and PIP are already installed on your computer then simply do the following in the terminal:

- pip install pandas

If not, then simply check out the Anaconda Documentation for how to set up .

https://docs.continuum.io/anaconda/install/



Installation of Pandas

What will the code output?

Pandas Plotting

Pandas provides a large suite of functions designed for exploratory analysis and data manipulation. These include:
- Pivot Tables
- Multi-Indexing
- Merging / Concatenation
- Date-Time
- Visualization
- Sub-Sets

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Related

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