Pandas Series - W3Schools

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Pandas Series ❮ Previous Next ❯

What is a Series?

A Pandas Series is like a column in a table.

It is a one-dimensional array holding data of any type.

Example

Create a simple Pandas Series from a list:

import pandas as pd a = [1, 7, 2]myvar = pd.Series(a) print(myvar) Try it Yourself »

Labels

If nothing else is specified, the values are labeled with their index number. First value has index 0, second value has index 1 etc.

This label can be used to access a specified value.

Example

Return the first value of the Series:

print(myvar[0]) Try it Yourself »

Create Labels

With the index argument, you can name your own labels.

Example

Create your own labels:

import pandas as pd a = [1, 7, 2]myvar = pd.Series(a, index = ["x", "y", "z"]) print(myvar) Try it Yourself »

When you have created labels, you can access an item by referring to the label.

Example

Return the value of "y":

print(myvar["y"]) Try it Yourself »

Key/Value Objects as Series

You can also use a key/value object, like a dictionary, when creating a Series.

Example

Create a simple Pandas Series from a dictionary:

import pandas as pd calories = {"day1": 420, "day2": 380, "day3": 390}myvar = pd.Series(calories) print(myvar) Try it Yourself »

Note: The keys of the dictionary become the labels.

To select only some of the items in the dictionary, use the index argument and specify only the items you want to include in the Series.

Example

Create a Series using only data from "day1" and "day2":

import pandas as pd calories = {"day1": 420, "day2": 380, "day3": 390}myvar = pd.Series(calories, index = ["day1", "day2"]) print(myvar) Try it Yourself »

DataFrames

Data sets in Pandas are usually multi-dimensional tables, called DataFrames.

Series is like a column, a DataFrame is the whole table.

Example

Create a DataFrame from two Series:

import pandas as pd data = { "calories": [420, 380, 390], "duration": [50, 40, 45]}myvar = pd.DataFrame(data) print(myvar) Try it Yourself »

You will learn about DataFrames in the next chapter.

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