Data Structures in Pandas

Lesson 2 of 19 in Coddy's Pandas Analytics course.

Pandas uses two types of data structures: series and dataframes.

A series in Pandas is like a one-dimensional array that holds any data type. For example, a series could be a list of integers [4, 3, 8, 5].
A dataframe is a two-dimensional data structure, like a table with rows and columns. Picture a spreadsheet or an SQL table. For instance, a DataFrame could be a collection of series (columns) such as name, age, and height.

Getting comfortable with Series and DataFrame will help you get the most out of Pandas.

To create a series use the following:

import pandas as pd
temp = pd.Series([1, 2, ,3])

Every column in a dataframe is a series object.

To convert a dictionary to a dataframe it must be in the right format:

data = [{"col1": 22000,'col2': 1500.0},
        {"col1": 25000,'col2': 3000.0},
        {"col1": 23000,'col2': 2500.0}]
df = pd.DataFrame(data)

There are other formats as well that are valid.

Challenge

Easy

Create a function named dataframe_creator that receives data, creates a dataframe from the data, and finally prints the dataframe.

To print a dataframe, write: print(df).

Try it yourself

import pandas as pd
def dataframe_creator(data):
    # Write code here

All lessons in Pandas Analytics

1Introduction

4Data Analysis with Pandas

2Working with the DataFrame

5Challenges

3Data Manipulation with Pandas