# Pandas Getting started
###### tags: `learn` `AI` `course`

import pandas as pd

DataFrame
A DataFrame is a table. It contains an array of individual entries, each of which has a certain value. Each entry corresponds to a row (or record) and a column.

pd.DataFrame({'Yes': [50, 21], 'No': [131, 2]})


The dictionary-list constructor assigns values to the column labels, but just uses an ascending count from 0 (0, 1, 2, 3, ...) for the row labels. Sometimes this is OK, but oftentimes we will want to assign these labels ourselves.

The list of row labels used in a DataFrame is known as an Index. We can assign values to it by using an index parameter in our constructor:

pd.DataFrame({'Bob': ['I liked it.', 'It was awful.'], 
              'Sue': ['Pretty good.', 'Bland.']},
             index=['Product A', 'Product B'])

Series
A Series, by contrast, is a sequence of data values. If a DataFrame is a table, a Series is a list. And in fact you can create one with nothing more than a list:

pd.Series([1, 2, 3, 4, 5])

Reading data files

wine_reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv")

We can use the shape attribute to check how large the resulting DataFrame is:

wine_reviews.shape


The pd.read_csv() function is well-endowed, with over 30 optional parameters you can specify. For example, you can see in this dataset that the CSV file has a built-in index, which pandas did not pick up on automatically. To make pandas use that column for the index (instead of creating a new one from scratch), we can specify an index_col.

wine_reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)
wine_reviews.head()