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Machine Learning

Merging, Joining and Concatenating DataFrames

Adding / Concatenating Rows (Part 1) 

import pandas as pd

image.png

men2004 = pd.read_csv("men2004.csv")

men2004
Athlete Medals
0 PHELPS, Michael 8
1 THORPE, Ian 4
2 SCHOEMAN, Roland 3
3 PEIRSOL, Aaron 3
4 CROCKER, Ian 3
5 KITAJIMA, Kosuke 3
6 HANSEN, Brendan 3
7 VAN DEN HOOGENBAND, Pieter 3
8 HACKETT, Grant 3
9 MORITA, Tomomi 2
10 LEZAK, Jason 2
11 ROGAN, Markus 2
12 KELLER, Klete 2
13 HALL, Gary Jr. 2
14 LOCHTE, Ryan 2
15 WALKER, Neil 2
16 YAMAMOTO, Takashi 2
17 SPRENGER, Nicholas 1
18 OKUMURA, Yoshihiro 1
19 PARRY, Stephen 1
20 PEARSON, Todd 1
21 ZASTROW, Mitja 1
22 PELLICIARI, Matteo 1
23 WOODWARD, Gabe 1
24 SERDINOV, Andriy 1
25 VENDT, Erik 1
26 ROSOLINO, Massimiliano 1
27 VEENS, Mark Hermanus 1
28 RUPPRATH, Thomas 1
29 VANDERKAAY, Peter 1
30 TOWNSEND, Darian 1
31 STEVENS, Craig 1
32 NEETHLING, Ryk 1
33 BOVELL, George 1
34 MEEUW, Helge 1
35 FLOREA, Razvan Ionut 1
36 CAPPELLAZZO, Federico 1
37 CERCATO, Simone 1
38 CONRAD, Lars 1
39 CSEH, Laszlo 1
40 DAVIES, David 1
41 DRAGANJA, Duje 1
42 DRIESEN, Steffen 1
43 DUBOSCQ, Hugues 1
44 DUSING, Nate 1
45 FERNS, Lyndon 1
46 GANGLOFF, Mark 1
47 MATKOVICH, Antony 1
48 GOLDBLATT, Scott 1
49 GYURTA, Daniel 1
50 JENSEN, Larsen 1
51 KENKHUIS, Johan 1
52 KETCHUM, Dan 1
53 KLIM, Michael 1
54 KRAYZELBURG, Lenny 1
55 KRUPPA, Jens 1
56 BREMBILLA, Emiliano 1
57 MAGNINI, Filippo 1
58 ZWERING, Klaas-Erik 1 
men2008 = pd.read_csv("men2008.csv")

men2008

men2004.append(men2008, ignore_index= True)

men0408 = pd.concat([men2004, men2008], ignore_index=False, keys = [2004, 2008], names = ["Year"])

men0408 

men0408.reset_index().drop(columns = "level_1")

Adding / Concatenating Rows (Part 2) 

men2004.head()

men2008.head()

men2004.columns =["Name", "Medals"]

men2004["Success"] = "Yes"

men2004

pd.concat([men2004, men2008], keys = [2004, 2008], names = ["Year"])

men2004.drop(labels = ["Success"], axis = 1, inplace = True)

men2004

men2008.columns = men2004.columns

men2008.head()

pd.concat([men2004, men2008], keys = [2004, 2008], names = ["Year"])

men2004 = pd.read_csv("men2004.csv", index_col = "Athlete")
men2008 = pd.read_csv("men2008.csv", index_col = "Athlete")

men2004.head()

men2008.head()

pd.concat([men2004, men2008], ignore_index= False, keys = [2004, 2008])

Arithmetic between Pandas Objects / Data Alignment 

import pandas as pd

topfive_2004 = pd.read_csv("topfive_2004.csv", index_col="Athlete")
topfive_2008 = pd.read_csv("topfive_2008.csv", index_col="Athlete")

topfive_2004

topfive_2008

topfive_2004 + topfive_2008

topfive_2004.add(topfive_2008, fill_value= 0)

topfive_2008.rename(columns = {"bronze":"Bronze"}, inplace = True)

topfive_2004.add(topfive_2008, fill_value= 0)

topfive_2004.sub(topfive_2008, fill_value= 0)

Outer Join 

import pandas as pd

men2004 = pd.read_csv("men2004.csv")
men2008 = pd.read_csv("men2008.csv")

men2004.head()

men2004.shape

men2008.head()

men2008.shape

image.png

len(men2008) + len(men2004)

men2004.merge(men2008, how = "outer", on = "Athlete", suffixes= ("_2004", "_2008"), indicator= True )

men0408 = men2004.merge(men2008, how = "outer", on = "Athlete", suffixes= ("_2004", "_2008"), indicator= True )

men0408._merge.value_counts()

image.png

Inner Join 

men2004.head()

men2008.head()

image.png

men2004.merge(men2008, how = "inner", on = "Athlete", suffixes= ("_2004", "_2008"), indicator= True )

Outer Join without Intersection 

men2004.head()

men2008.head()

image.png

men0408= men2004.merge(men2008, how = "outer", on = "Athlete", suffixes= ("_2004", "_2008"), indicator= True )

men0408.loc[men0408._merge != "both"]

Left Join without Intersection 

men2004.head()

men2008.head()

image.png

men0408 = men2004.merge(men2008, how = "outer", on = "Athlete", suffixes= ("_2004", "_2008"), indicator= True )

men0408.head()

men0408[men0408._merge == "left_only"].shape

Right Join without Intersection 

men2004.head()

men2008.head()

image.png

men0408 =  men2004.merge(men2008, how = "outer", on = "Athlete", suffixes = ["_2004", "_2008"], indicator = True)

men0408[men0408._merge == "right_only"].shape

Left Join 

men2004.head()

men2008.head()

image.png

men2004.merge(men2008, how = "left", on = "Athlete", suffixes = ["_2004", "_2008"], indicator = True)

Right Join 

men2004.head()

men2008.head()

image.png

men2004.merge(men2008, how = "right", on = "Athlete", suffixes = ["_2004", "_2008"], indicator = True)

Joining on different Column Labels & Indexes 

import pandas as pd

men2004 = pd.read_csv("men2004.csv")
men2008 = pd.read_csv("men2008.csv")

men2004.head()
Name Medals
0 PHELPS, Michael 8
1 THORPE, Ian 4
2 SCHOEMAN, Roland 3
3 PEIRSOL, Aaron 3
4 CROCKER, Ian 3 
men2008.head()
Athlete Medals
0 PHELPS, Michael 8
1 LOCHTE, Ryan 4
2 BERNARD, Alain 3
3 SULLIVAN, Eamon 3
4 LAUTERSTEIN, Andrew 3 
men2004.columns = ["Name", "Medals"]

men2004.head()
Name Medals
0 PHELPS, Michael 8
1 THORPE, Ian 4
2 SCHOEMAN, Roland 3
3 PEIRSOL, Aaron 3
4 CROCKER, Ian 3 
men0408 = men2004.merge(men2008, how = "outer", left_on = "Name", right_on = "Athlete",
                      suffixes = ["_2004", "_2008"], indicator = True)

men0408
Name Medals_2004 Medals_2008
0 PHELPS, Michael 8.0 8.0
1 THORPE, Ian 4.0 NaN
2 SCHOEMAN, Roland 3.0 NaN
3 PEIRSOL, Aaron 3.0 3.0
4 CROCKER, Ian 3.0 1.0
... ... ... ...
100 LAGUNOV, Evgeniy NaN 1.0
101 BERENS, Ricky NaN 1.0
102 LURZ, Thomas NaN 1.0
103 MALLET, Gregory NaN 1.0
104 ZHANG, Lin NaN 1.0

105 rows × 3 columns

men0408.Name.fillna(men0408.Athlete, inplace = True)

men0408.drop(["Athlete", "_merge"], axis = 1, inplace = True)

men2004.head()
Name Medals
0 PHELPS, Michael 8
1 THORPE, Ian 4
2 SCHOEMAN, Roland 3
3 PEIRSOL, Aaron 3
4 CROCKER, Ian 3 
men2008.set_index("Athlete", inplace = True)

men2008.head()

men2004.merge(men2008, how = "outer", left_on= "Name", right_index = True,
              suffixes = ["_2004", "_2008"], indicator = True)
Name Medals_2004 Medals_2008 _merge
0.0 PHELPS, Michael 8.0 8.0 both
1.0 THORPE, Ian 4.0 NaN left_only
2.0 SCHOEMAN, Roland 3.0 NaN left_only
3.0 PEIRSOL, Aaron 3.0 3.0 both
4.0 CROCKER, Ian 3.0 1.0 both
... ... ... ... ...
NaN LAGUNOV, Evgeniy NaN 1.0 right_only
NaN BERENS, Ricky NaN 1.0 right_only
NaN LURZ, Thomas NaN 1.0 right_only
NaN MALLET, Gregory NaN 1.0 right_only
NaN ZHANG, Lin NaN 1.0 right_only

105 rows × 4 columns

Joining on many Columns 

import pandas as pd

men2004_det = pd.read_csv("men2004_det.csv")
men2008_det = pd.read_csv("men2008_det.csv")

men2004_det.head(10)

men2008_det.head(10)

men2004_det.loc[men2004_det.Athlete == "PHELPS, Michael"]

men2008_det.loc[men2008_det.Athlete == "PHELPS, Michael"]

men0408 = men2004_det.merge(men2008_det, how = "inner", on = ["Athlete", "Medal"], suffixes= ("_2004", "_2008"))

men0408.loc[men0408.Athlete == "PHELPS, Michael"]

pd.merge and join() 

import pandas as pd

men2004 = pd.read_csv("men2004.csv", index_col = "Athlete")
men2008 = pd.read_csv("men2008.csv", index_col = "Athlete")

men2004.head()

men2008.head()

men2004.merge(men2008, how = "outer", on = "Athlete", suffixes= ("_2004", "_2008"), indicator= True)

pd.merge(men2004, men2008, how ="outer", on = "Athlete", suffixes= ("_2004", "_2008"), indicator= True )

men2004.join(men2008, how = "outer", lsuffix = "_2004", rsuffix = "_2008")

Machine Learning - Computer Science Faculty of Shahid Beheshti University. Winter 2023 - Contact us at saeidcheshmi@outlook.com