Series in `pandas` differ from `numpy` arrays in that they have indexes. These can be integers, floating-point numbers, strings, time series.

To get the series' indexes, use the `.index` attribute of a series object. To get values, use the `values` attribute. By default, indexes are integers starting from 0, but if you want to change them, you simply may reassign new list of indexes to the `.index` attribute. For instance,

# Importing library
import pandas as pd

# Creating pandas series
ser = pd.Series([1000, 2500, 1700])
# Getting series' indexes and values
print(ser.index)
print(ser.values)
print(ser)

# Changing series' indexes
ser.index = ['first', 'second', 'third']
print(ser)

As you can see, initial indexes were `0, 1, 2`. After changing, they became `'first', 'second', 'third'`.

This course is designed to teach you the very basics of pandas - a must-have  Python library for every data analyst or data scientist (and not only). You will start from defining the basic objects, learn how to read data from files, and how to manipulate and aggregate loaded data.

In the first section of the course you will learn two basic objects provided by pandas - Series and DataFrame. You will learn how to create them and change their indexes or column names.

Often the necessary data are stored in some files. You need to be able to load these data and gain the first insights.

Once you have loaded the data as Series/DataFrame, you need to be able to get certain data based on needs. You will learn how to access rows, columns, and filter observations by conditions.

In the last section of the course, you will get to know how to calculate some aggregate statistics of your data, such as mean, median, sum, product. Also you will learn how to count values and get only unique observations.

Introduction to pandas [track]

Indexes and Values

Introduction to pandas [track]

Indexes and Values