Pandas is a python library for analyzing data; it supports multiple data inputs.

The article is based on an English-language article Building a neural network with python (broken link), and on a pandas tutorial (broken link).

This tutorial will use a dataset in csv format about wines (chemical analysis to determine the origin of wines).

Below, the way to handle the data from the file wine_data.csv is described step by step.

You can download the file Pandas.py and Pandas.ipynb from github.

Import Pandas as pd:

>>> import pandas as pd

Extract the data from the csv file and define the column names:

>>> wine = pd.read_csv('wine_data.csv', names = ["Cultivator", "Alchol", "Malic_Acid", "Ash", "Alcalinity_of_Ash", "Magnesium", "Total_phenols", "Falvanoids", "Nonflavanoid_phenols", "Proanthocyanins", "Color_intensity", "Hue", "OD280", "Proline"])

Show the first rows of data:

>>> wine.head()

Show the dimensions of the table:

>>> wine.shape
(178, 14)

There are 178 rows and 14 columns.

Show the first 2 rows of the table:

wine.loc[[0,1]]

Show the first two rows and the Cultivator and Alchol columns

>>> wine.loc[[0,1]][["Cultivator","Alchol"]]

This introduction is necessary since future articles will work a lot with the pandas library.

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