Pandas Core Components - The Data Frame Object

• The DataFrame Object
• 1. Constructing a DataFrame From a Series Object
• 2. Constructing a DataFrame From a Dictionary
• 3. Constructing a Dataframe by Importing Data From a File
The DataFrame Object #
In the previous lesson, we learned about Series. The next fundamental
structure in Pandas that we will learn about is the DataFrame. While a Series
is essentially a column, a DataFrame is a multi-dimensional table made
up of a collection of Series. Dataframes allow us to store and manipulate
tabular data where rows consist of observations and columns represent
variables.
There are several ways to create a DataFrame using pd.DataFrame() . For
example, we can create a DataFrame by passing multiple Series into the
DataFrame object, we can convert a dictionary to a DataFrame or we can
import data from a csv file. Let’s look at each of these in detail.

1. Constructing a DataFrame From a Series Object # We can create a DataFrame from a single Series by passing the Series object as input to the DataFrame creation method, along with an optional input parameter, column, which allows us to name the columns: import pandas as pd data_s1 = pd.Series([12, 24, 33, 15], index=['apples', 'bananas', 'strawberries', 'oranges']) # 'quantity' is the name for our column dataframe1 = pd.DataFrame(data_s1, columns=['quantity']) print(dataframe1)
2. Constructing a DataFrame From a Dictionary # We can construct a DataFrame form any list of dictionaries. Say we have a dictionary with countries, their capitals and some other variable (population, size of that country, number of schools, etc.): dict = {"country": ["Norway", "Sweden", "Spain", "France"], "capital": ["Oslo", "Stockholm", "Madrid", "Paris"], "SomeColumn": ["100", "200", "300", "400"]} data = pd.DataFrame(dict) print(data) We can also construct a DataFrame from a dictionary of Series objects. Say we have two different Series; one for the price of fruits and one for their quantity. We want to put all the fruits related data together into a single table. We can do this like so: import pandas as pd quantity = pd.Series([12, 24, 33, 15], index=['apples', 'bananas', 'strawberries', 'oranges']) price = pd.Series([4, 4.5, 8, 7.5], index=['apples', 'bananas', 'strawberries', 'oranges'])

df = pd.DataFrame({'quantity': quantity,
'price': price})
print(df)

1. Constructing a Dataframe by Importing Data From a File # It’s quite simple to load data from various file formats, e.g., CSV, Excel, json into a DataFrame. We will be importing actual data for analyzing the IMDB- movies dataset in the next lesson. Here is what loading data from different file formats looks like in code: import pandas as pd # Given we have a file called data1.csv in our working directory: df = pd.read_csv('data1.csv') #given json data df = pd.read_json('data2.json') We have only just scratched the surface and learned how to construct DataFrames. In the next lessons we will go deeper and learn-by-doing the many methods that we can call on these powerful objects.