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#### Introduction to Data Wrangling with Pandas ####
## Page 4 ##
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#### Merging Data Frames ####
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# Pandas concat(), merge(), append() functions come handy here
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#so that we can view the graphs inside the notebook
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df1 = pd.DataFrame({'A': range(0,4),
'B': range(0,4),
'C': range(0,4),
'D': range(0,4)},
index=[0, 1, 2, 3])
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df1
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df2 = pd.DataFrame({'A': range(4,8),
'B': range(4,8),
'C': range(4,8),
'D': range(4,8)},
index=[4, 5, 6, 7])
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df2
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result = pd.concat([df1, df2])
result
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result = pd.concat([df1, df2], keys=['first','second'])
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result
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result.loc['second']
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df1
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df3 = pd.DataFrame({'B': range(0,4),
'D': range(0,4),
'F': range(0,4)},
index=[2, 3, 6, 7])
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df3
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result = pd.concat([df1, df3])
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result
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df1
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df3
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result = pd.concat([df1, df3], axis=1)
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result
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result = pd.concat([df1, df3], axis =1, join='inner')
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result
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result = pd.concat([df1, df3], join='inner')
result
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# SQL Like Join Operations inner outer, left, right on given keys
# https://pandas.pydata.org/pandas-docs/stable/comparison_with_sql.html
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# Real power- merge datasets obtained from different sources.