Unlike the arrays supported by NumPy, the columns in a DataFrame can be of all different types. You can have one column holding string values, another column holding a date, another column holding a floating point number and so on.

Pandas Dataframe vs. numpy.array

Which of the following are correct methods that can be used to create a pandas DataFrame? 

Creating a Pandas DataFrame in Python

For example, lets create some data:

import pandas as pd

data = {"Trees": [['Blue','Brown','Red']], "Age": [[3,6,10]]}

- The next step is to put this data in to the Data frame object:

frame = pd.DataFrame(data)

print(frame)





Code Example of DataFrame

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

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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

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 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

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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