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Combining Data for Analysis

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Concatenate Data with pandas.concat()

8:39 with Richard Tillies

Welcome! In this video, we'll introduce you to the concat() function and the arguments used to successfully concatenate data between two datasets.

Data files for download

Load 2019 data into pandas

billboard19 = pd.read_csv("billboard_100_2019.csv", index_col ="ID")
spotify19 = pd.read_csv("spotify_200_2019.csv", index_col ="ID")

Create DataFrames for 2019 Ariana Grande Billboard and Spotify song data

ariana_bill19 = billboard19[billboard19["Artists"].str.contains("Ariana Grande")] 
ariana_spot19 = spotify19[spotify19["Artists"].str.contains("Ariana Grande")]

Concatenate 2017-2018 Ariana Grande song data with 2019 Ariana Grande song data in Billboard

ariana_bill_all = pd.concat([ariana_bill, ariana_bill19])

Concatenate 2017-2018 Ariana Grande song data with 2019 Ariana Grande song data in Spotify

ariana_spot_all = pd.concat([ariana_spot, ariana_spot19])

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    But, if you're in this workshop you're probably familiar with string 0:00
    concatenation, which is the combination of two or more strings. 0:02
    In Python, this is performed with the plus operator. 0:06
    For example, print 0:10
    ("book" + "keeper") 0:14
    output is bookkeeper. 0:19
    We simply combine two small words into a new, larger word. 0:25
    It's like we added the words together. 0:29

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