I have the following 2 pandas dataframes:
city Population
0 New York City 20153634
1 Los Angeles 13310447
2 San Francisco Bay Area 6657982
3 Chicago 9512999
4 Dallas–Fort Worth 7233323
5 Washington, D.C. 6131977
6 Philadelphia 6070500
7 Boston 4794447
8 Minneapolis–Saint Paul 3551036
9 Denver 2853077
10 Miami–Fort Lauderdale 6066387
11 Phoenix 4661537
12 Detroit 4297617
13 Toronto 5928040
14 Houston 6772470
15 Atlanta 5789700
16 Tampa Bay Area 3032171
17 Pittsburgh 2342299
18 Cleveland 2055612
19 Seattle 3798902
20 Cincinnati 2165139
21 Kansas City 2104509
22 St. Louis 2807002
23 Baltimore 2798886
24 Charlotte 2474314
25 Indianapolis 2004230
26 Nashville 1865298
27 Milwaukee 1572482
28 New Orleans 1268883
29 Buffalo 1132804
30 Montreal 4098927
31 Vancouver 2463431
32 Orlando 2441257
33 Portland 2424955
34 Columbus 2041520
35 Calgary 1392609
36 Ottawa 1323783
37 Edmonton 1321426
38 Salt Lake City 1186187
39 Winnipeg 778489
40 San Diego 3317749
41 San Antonio 2429609
42 Sacramento 2296418
43 Las Vegas 2155664
44 Jacksonville 1478212
45 Oklahoma City 1373211
46 Memphis 1342842
47 Raleigh 1302946
48 Green Bay 318236
49 Hamilton 747545
50 Regina 236481
city W/L Ratio
0 Boston 2.500000
1 Buffalo 0.555556
2 Calgary 1.057143
3 Chicago 0.846154
4 Columbus 1.500000
5 Dallas–Fort Worth 1.312500
6 Denver 1.433333
7 Detroit 0.769231
8 Edmonton 0.900000
9 Las Vegas 2.125000
10 Los Angeles 1.655862
11 Miami–Fort Lauderdale 1.466667
12 Minneapolis-Saint Paul 1.730769
13 Montreal 0.725000
14 Nashville 2.944444
15 New York 1.517241
16 New York City 0.908870
17 Ottawa 0.651163
18 Philadelphia 1.615385
19 Phoenix 0.707317
20 Pittsburgh 1.620690
21 Raleigh 1.028571
22 San Francisco Bay Area 1.666667
23 St. Louis 1.375000
24 Tampa Bay 2.347826
25 Toronto 1.884615
26 Vancouver 0.775000
27 Washington, D.C. 1.884615
28 Winnipeg 2.600000
And I do a join like this:
result = pd.merge(df, nhl_df , on="city")
The result should have 28 rows, instead I have 24 rows.
One of the missing one is for example Miami-Fort Lauderdale
I have double checked on both dataframes and there are NO typographical errors. So, why isnt it in the end dataframe?
city Population W/L Ratio
0 New York City 20153634 0.908870
1 Los Angeles 13310447 1.655862
2 San Francisco Bay Area 6657982 1.666667
3 Chicago 9512999 0.846154
4 Dallas–Fort Worth 7233323 1.312500
5 Washington, D.C. 6131977 1.884615
6 Philadelphia 6070500 1.615385
7 Boston 4794447 2.500000
8 Denver 2853077 1.433333
9 Phoenix 4661537 0.707317
10 Detroit 4297617 0.769231
11 Toronto 5928040 1.884615
12 Pittsburgh 2342299 1.620690
13 St. Louis 2807002 1.375000
14 Nashville 1865298 2.944444
15 Buffalo 1132804 0.555556
16 Montreal 4098927 0.725000
17 Vancouver 2463431 0.775000
18 Columbus 2041520 1.500000
19 Calgary 1392609 1.057143
20 Ottawa 1323783 0.651163
21 Edmonton 1321426 0.900000
22 Winnipeg 778489 2.600000
23 Las Vegas 2155664 2.125000
24 Raleigh 1302946 1.028571
I think here is possible check if same chars by integer that represents the character in function ord, here are different – with code 150 and – with code 8211, so it is reason why values not matched:
a = df1.loc[10, 'city']
print (a)
Miami–Fort Lauderdale
print ([ord(x) for x in a])
[77, 105, 97, 109, 105, 150, 70, 111, 114, 116, 32, 76, 97, 117, 100, 101, 114, 100, 97, 108, 101]
b = df2.loc[11, 'city']
print (b)
Miami–Fort Lauderdale
print ([ord(x) for x in b])
[77, 105, 97, 109, 105, 8211, 70, 111, 114, 116, 32, 76, 97, 117, 100, 101, 114, 100, 97, 108, 101]
You can try copy values for replace for select correct - value:
#first – is copied from b, second – from a
df2['city'] = df2['city'].replace('–','–', regex=True)
I have the following pandas dataframe, only showing one column
0 Atlantic Division
1 Tampa Bay Lightning*
2 Boston Bruins*
3 Toronto Maple Leafs*
4 Florida Panthers
5 Detroit Red Wings
6 Montreal Canadiens
7 Ottawa Senators
8 Buffalo Sabres
9 Metropolitan Division
10 Washington Capitals*
11 Pittsburgh Penguins*
12 Philadelphia Flyers*
13 Columbus Blue Jackets*
14 New Jersey Devils*
15 Carolina Hurricanes
16 New York Islanders
17 New York Rangers
18 Central Division
19 Nashville Predators*
20 Winnipeg Jets*
21 Minnesota Wild*
22 Colorado Avalanche*
23 St. Louis Blues
24 Dallas Stars
25 Chicago Blackhawks
26 Pacific Division
27 Vegas Golden Knights*
28 Anaheim Ducks*
29 San Jose Sharks*
30 Los Angeles Kings*
31 Calgary Flames
32 Edmonton Oilers
33 Vancouver Canucks
34 Arizona Coyotes
35 Atlantic Division
36 Montreal Canadiens*
37 Ottawa Senators*
38 Boston Bruins*
39 Toronto Maple Leafs*
40 Tampa Bay Lightning
41 Florida Panthers
42 Detroit Red Wings
43 Buffalo Sabres
44 Metropolitan Division
45 Washington Capitals*
46 Pittsburgh Penguins*
47 Columbus Blue Jackets*
48 New York Rangers*
49 New York Islanders
50 Philadelphia Flyers
51 Carolina Hurricanes
52 New Jersey Devils
53 Central Division
54 Chicago Blackhawks*
55 Minnesota Wild*
56 St. Louis Blues*
57 Nashville Predators*
58 Winnipeg Jets
59 Dallas Stars
60 Colorado Avalanche
61 Pacific Division
62 Anaheim Ducks*
63 Edmonton Oilers*
64 San Jose Sharks*
65 Calgary Flames*
66 Los Angeles Kings
67 Arizona Coyotes
68 Vancouver Canucks
69 Atlantic Division
70 Florida Panthers*
71 Tampa Bay Lightning*
72 Detroit Red Wings*
73 Boston Bruins
74 Ottawa Senators
75 Montreal Canadiens
76 Buffalo Sabres
77 Toronto Maple Leafs
78 Metropolitan Division
79 Washington Capitals*
80 Pittsburgh Penguins*
81 New York Rangers*
82 New York Islanders*
83 Philadelphia Flyers*
84 Carolina Hurricanes
85 New Jersey Devils
86 Columbus Blue Jackets
87 Central Division
88 Dallas Stars*
89 St. Louis Blues*
90 Chicago Blackhawks*
91 Nashville Predators*
92 Minnesota Wild*
93 Colorado Avalanche
94 Winnipeg Jets
95 Pacific Division
96 Anaheim Ducks*
97 Los Angeles Kings*
98 San Jose Sharks*
99 Arizona Coyotes
100 Calgary Flames
101 Vancouver Canucks
102 Edmonton Oilers
103 Atlantic Division
104 Montreal Canadiens*
105 Tampa Bay Lightning*
106 Detroit Red Wings*
107 Ottawa Senators*
108 Boston Bruins
109 Florida Panthers
110 Toronto Maple Leafs
111 Buffalo Sabres
112 Metropolitan Division
113 New York Rangers*
114 Washington Capitals*
115 New York Islanders*
116 Pittsburgh Penguins*
117 Columbus Blue Jackets
118 Philadelphia Flyers
119 New Jersey Devils
120 Carolina Hurricanes
121 Central Division
122 St. Louis Blues*
123 Nashville Predators*
124 Chicago Blackhawks*
125 Minnesota Wild*
126 Winnipeg Jets*
127 Dallas Stars
128 Colorado Avalanche
129 Pacific Division
130 Anaheim Ducks*
131 Vancouver Canucks*
132 Calgary Flames*
133 Los Angeles Kings
134 San Jose Sharks
135 Edmonton Oilers
136 Arizona Coyotes
137 Atlantic Division
138 Boston Bruins*
139 Tampa Bay Lightning*
140 Montreal Canadiens*
141 Detroit Red Wings*
142 Ottawa Senators
143 Toronto Maple Leafs
144 Florida Panthers
145 Buffalo Sabres
146 Metropolitan Division
147 Pittsburgh Penguins*
148 New York Rangers*
149 Philadelphia Flyers*
150 Columbus Blue Jackets*
151 Washington Capitals
152 New Jersey Devils
153 Carolina Hurricanes
154 New York Islanders
155 Central Division
156 Colorado Avalanche*
157 St. Louis Blues*
158 Chicago Blackhawks*
159 Minnesota Wild*
160 Dallas Stars*
161 Nashville Predators
162 Winnipeg Jets
163 Pacific Division
164 Anaheim Ducks*
165 San Jose Sharks*
166 Los Angeles Kings*
167 Phoenix Coyotes
168 Vancouver Canucks
169 Calgary Flames
170 Edmonton Oilers
Name: team, dtype: object
I need to create one additional column with the city name.
At first look the regex would be simple (the first word) should be the city name, and the rest is the team name.
However some cities have 2 words (Los Angeles, St Louis ,etc)
Is there a possibility to do this with regex or it has to be done manually?
Update: I tried the following:
nhl_df['city']=nhl_df['team'].str.extract(r'^(?:([\w.]{1,5}\s\w+)|(\w+)|)(?:\s\w+)+\*?$')
But I get this error:
ValueError: Wrong number of items passed 2, placement implies 1
You can try something like that:
^(?:([\w.]{1,5}\s\w+)|(\w+)|)(?:\s\w+)+\*?$
Here you should look for city name in first or second group.
This pattern uses assumption that first part of two-word city names has no more than 5 symbols. The result might not be so clean, but seems to work fine on given example.
You can use
^([\w.]{1,5}(?:\s\w+)?\w*)
See the regex demo. Details:
^ - start of string
([\w.]{1,5}(?:\s\w+)?\w*) - Capturing group 1:
[\w.]{1,5} - one to five word or dot chars
(?:\s\w+)? - an optional occurrence of a whitespace and then one or more word chars
\w* - zero or more word chars.
Pandas test:
import pandas as pd
nhl_df = pd.DataFrame({"team":["Atlantic Division","Tampa Bay Lightning*","Boston Bruins*","Toronto Maple Leafs*","Florida Panthers","Detroit Red Wings","Montreal Canadiens","Ottawa Senators","Buffalo Sabres","Metropolitan Division","Washington Capitals*","Pittsburgh Penguins*","Philadelphia Flyers*","Columbus Blue Jackets*","New Jersey Devils*","Carolina Hurricanes","New York Islanders","New York Rangers","Central Division","Nashville Predators*","Winnipeg Jets*","Minnesota Wild*","Colorado Avalanche*","St. Louis Blues","Dallas Stars","Chicago Blackhawks","Pacific Division","Vegas Golden Knights*","Anaheim Ducks*","San Jose Sharks*","Los Angeles Kings*","Calgary Flames","Edmonton Oilers","Vancouver Canucks","Arizona Coyotes","Atlantic Division","Montreal Canadiens*","Ottawa Senators*","Boston Bruins*","Toronto Maple Leafs*","Tampa Bay Lightning","Florida Panthers","Detroit Red Wings","Buffalo Sabres","Metropolitan Division","Washington Capitals*","Pittsburgh Penguins*","Columbus Blue Jackets*","New York Rangers*","New York Islanders","Philadelphia Flyers","Carolina Hurricanes","New Jersey Devils","Central Division","Chicago Blackhawks*","Minnesota Wild*","St. Louis Blues*","Nashville Predators*","Winnipeg Jets","Dallas Stars","Colorado Avalanche","Pacific Division","Anaheim Ducks*","Edmonton Oilers*","San Jose Sharks*","Calgary Flames*","Los Angeles Kings","Arizona Coyotes","Vancouver Canucks","Atlantic Division","Florida Panthers*","Tampa Bay Lightning*","Detroit Red Wings*","Boston Bruins","Ottawa Senators","Montreal Canadiens","Buffalo Sabres","Toronto Maple Leafs","Metropolitan Division","Washington Capitals*","Pittsburgh Penguins*","New York Rangers*","New York Islanders*","Philadelphia Flyers*","Carolina Hurricanes","New Jersey Devils","Columbus Blue Jackets","Central Division","Dallas Stars*","St. Louis Blues*","Chicago Blackhawks*","Nashville Predators*","Minnesota Wild*","Colorado Avalanche","Winnipeg Jets","Pacific Division","Anaheim Ducks*","Los Angeles Kings*","San Jose Sharks*","Arizona Coyotes","Calgary Flames","Vancouver Canucks","Edmonton Oilers","Atlantic Division","Montreal Canadiens*","Tampa Bay Lightning*","Detroit Red Wings*","Ottawa Senators*","Boston Bruins","Florida Panthers","Toronto Maple Leafs","Buffalo Sabres","Metropolitan Division","New York Rangers*","Washington Capitals*","New York Islanders*","Pittsburgh Penguins*","Columbus Blue Jackets","Philadelphia Flyers","New Jersey Devils","Carolina Hurricanes","Central Division","St. Louis Blues*","Nashville Predators*","Chicago Blackhawks*","Minnesota Wild*","Winnipeg Jets*","Dallas Stars","Colorado Avalanche","Pacific Division","Anaheim Ducks*","Vancouver Canucks*","Calgary Flames*","Los Angeles Kings","San Jose Sharks","Edmonton Oilers","Arizona Coyotes","Atlantic Division","Boston Bruins*","Tampa Bay Lightning*","Montreal Canadiens*","Detroit Red Wings*","Ottawa Senators","Toronto Maple Leafs","Florida Panthers","Buffalo Sabres","Metropolitan Division","Pittsburgh Penguins*","New York Rangers*","Philadelphia Flyers*","Columbus Blue Jackets*","Washington Capitals","New Jersey Devils","Carolina Hurricanes","New York Islanders","Central Division","Colorado Avalanche*","St. Louis Blues*","Chicago Blackhawks*","Minnesota Wild*","Dallas Stars*","Nashville Predators","Winnipeg Jets","Pacific Division","Anaheim Ducks*","San Jose Sharks*","Los Angeles Kings*","Phoenix Coyotes","Vancouver Canucks","Calgary Flames","Edmonton Oilers"]})
nhl_df['city']=nhl_df['team'].str.extract(r'^([\w.]{1,5}(?:\s\w+)?\w*)')
>>> nhl_df
team city
0 Atlantic Division Atlantic
1 Tampa Bay Lightning* Tampa Bay
2 Boston Bruins* Boston
3 Toronto Maple Leafs* Toronto
4 Florida Panthers Florida
.. ... ...
166 Los Angeles Kings* Los Angeles
167 Phoenix Coyotes Phoenix
168 Vancouver Canucks Vancouver
169 Calgary Flames Calgary
170 Edmonton Oilers Edmonton
^\S+(?=\s\S+$)
This regex gives you the first word of all teamnames that only consist of two words. The others you have to sort manually, because there is no way to tell just by pattern if the middle word is part of the city or the teamname.
Try using the below regex
/(\d*\s*)([a-zA-Z\s]*)(\s)(\b([a-zA-z\*]*))$/
Checkthis
function Replace(str) {
var result = str.replace(/(\d*\s*)([a-zA-Z\s]*)(\s)(\b([a-zA-z\*]*))$/gim, function (a, $1, $2, $3, $4) {
return `${$2}--${$4}`;
});
return result;
}
I have Fifa dataset and it includes information about football players. One of the features of this dataset is the value of football players but it is in string form such as "$300K" or "$50M". How can I delete simply these euro and "M, K" symbol and write their values in same units?
import numpy as np
import pandas as pd
location = r'C:\Users\bemrem\Desktop\Python\fifa\fifa_dataset.csv'
_dataframe = pd.read_csv(location)
_dataframe = _dataframe.dropna()
_dataframe = _dataframe.reset_index(drop=True)
_dataframe = _dataframe[['Name', 'Value', 'Nationality', 'Age', 'Wage',
'Overall', 'Potential']]
_array = ['Belgium', 'France', 'Brazil', 'Croatia', 'England',' Portugal',
'Uruguay', 'Switzerland', 'Spain', 'Denmark']
_dataframe = _dataframe.loc[_dataframe['Nationality'].isin(_array)]
_dataframe = _dataframe.reset_index(drop=True)
print(_dataframe.head())
print()
print(_dataframe.tail())
I tried to convert this Value column but I failed. This is what I get
Name Value Nationality Age Wage Overall Potential
0 Neymar €123M Brazil 25 €280K 92 94
1 L. Suárez €97M Uruguay 30 €510K 92 92
2 E. Hazard €90.5M Belgium 26 €295K 90 91
3 Sergio Ramos €52M Spain 31 €310K 90 90
4 K. De Bruyne €83M Belgium 26 €285K 89 92
Name Value Nationality Age Wage Overall Potential
4931 A. Kilgour €40K England 19 €1K 47 56
4932 R. White €60K England 18 €2K 47 65
4933 T. Sawyer €50K England 18 €1K 46 58
4934 J. Keeble €40K England 18 €1K 46 56
4935 J. Lundstram €60K England 18 €1K 46 64
But I want to my output looks like this:
Name Value Nationality Age Wage Overall Potential
0 Neymar 123 Brazil 25 €280K 92 94
1 L. Suárez 97 Uruguay 30 €510K 92 92
2 E. Hazard 90.5 Belgium 26 €295K 90 91
3 Sergio Ramos 52 Spain 31 €310K 90 90
4 K. De Bruyne 83 Belgium 26 €285K 89 92
Name Value Nationality Age Wage Overall Potential
4931 A. Kilgour 0.04 England 19 €1K 47 56
4932 R. White 0.06 England 18 €2K 47 65
4933 T. Sawyer 0.05 England 18 €1K 46 58
4934 J. Keeble 0.04 England 18 €1K 46 56
4935 J. Lundstram 0.06 England 18 €1K 46 64
I do not have enough reputation to flag an answer as a duplicate. However, I believe that this will solve your particular question in addition to providing a solution if there is no "K" or "M" in your string.
You will also need to replace $ with € in the regex.
Convert the string 2.90K to 2900 or 5.2M to 5200000 in pandas dataframe
I am troubling with counting the number of counties using famous cenus.csv data.
Task: Count number of counties in each state.
Facing comparing (I think) / Please read below?
I've tried this:
df = pd.read_csv('census.csv')
dfd = df[:]['STNAME'].unique() //Gives out names of state
serr = pd.Series(dfd) // converting to series (from array)
After this, i've tried using two approaches:
1:
df[df['STNAME'] == serr] **//ERROR: series length must match**
2:
i = 0
for name in serr: //This generate error 'Alabama'
df['STNAME'] == name
for i in serr:
serr[i] == serr[name]
print(serr[name].count)
i+=1
Please guide me; it has been three days with this stuff.
Use groupby and aggregate COUNTY using nunique:
In [1]: import pandas as pd
In [2]: df = pd.read_csv('census.csv')
In [3]: unique_counties = df.groupby('STNAME')['COUNTY'].nunique()
Now the results
In [4]: unique_counties
Out[4]:
STNAME
Alabama 68
Alaska 30
Arizona 16
Arkansas 76
California 59
Colorado 65
Connecticut 9
Delaware 4
District of Columbia 2
Florida 68
Georgia 160
Hawaii 6
Idaho 45
Illinois 103
Indiana 93
Iowa 100
Kansas 106
Kentucky 121
Louisiana 65
Maine 17
Maryland 25
Massachusetts 15
Michigan 84
Minnesota 88
Mississippi 83
Missouri 116
Montana 57
Nebraska 94
Nevada 18
New Hampshire 11
New Jersey 22
New Mexico 34
New York 63
North Carolina 101
North Dakota 54
Ohio 89
Oklahoma 78
Oregon 37
Pennsylvania 68
Rhode Island 6
South Carolina 47
South Dakota 67
Tennessee 96
Texas 255
Utah 30
Vermont 15
Virginia 134
Washington 40
West Virginia 56
Wisconsin 73
Wyoming 24
Name: COUNTY, dtype: int64
juanpa.arrivillaga has a great solution. However, the code needs a minor modification.
The "counties" with 'SUMLEV' == 40 or 'COUNTY' == 0 should be filtered. Otherwise, all the number of counties are too big by one.
So, the correct answer should be:
unique_counties = census_df[census_df['SUMLEV'] == 50].groupby('STNAME')['COUNTY'].nunique()
with the following result:
STNAME
Alabama 67
Alaska 29
Arizona 15
Arkansas 75
California 58
Colorado 64
Connecticut 8
Delaware 3
District of Columbia 1
Florida 67
Georgia 159
Hawaii 5
Idaho 44
Illinois 102
Indiana 92
Iowa 99
Kansas 105
Kentucky 120
Louisiana 64
Maine 16
Maryland 24
Massachusetts 14
Michigan 83
Minnesota 87
Mississippi 82
Missouri 115
Montana 56
Nebraska 93
Nevada 17
New Hampshire 10
New Jersey 21
New Mexico 33
New York 62
North Carolina 100
North Dakota 53
Ohio 88
Oklahoma 77
Oregon 36
Pennsylvania 67
Rhode Island 5
South Carolina 46
South Dakota 66
Tennessee 95
Texas 254
Utah 29
Vermont 14
Virginia 133
Washington 39
West Virginia 55
Wisconsin 72
Wyoming 23
Name: COUNTY, dtype: int64
#Bakhtawar - This is a very simple way:
df.groupby(df['STNAME']).count().COUNTY