Hi I'm trying to find the unique Player which show up in every Team.
df =
Team Player Number
A Joe 8
A Mike 10
A Steve 11
B Henry 9
B Steve 19
B Joe 4
C Mike 18
C Joe 6
C Steve 18
C Dan 1
C Henry 3
and the result should be:
result =
Team Player Number
A Joe 8
A Steve 11
B Joe 4
B Steve 19
C Joe 6
C Steve 18
since Joe and Steve are the only Player in each Team
You can use a GroupBy.transform to get a count of unique teams that each player is a member of, and compare this to the overall count of unique teams. This will give you a Boolean array, which you can use to filter your DataFrame:
df = df[df.groupby('Player')['Team'].transform('nunique') == df['Team'].nunique()]
The resulting output:
Team Player Number
0 A Joe 8
2 A Steve 11
4 B Steve 19
5 B Joe 4
7 C Joe 6
8 C Steve 18
Related
df is like so:
Week Name
1 TOM
1 BEN
1 CARL
2 TOM
2 BEN
2 CARL
3 TOM
3 BEN
3 CARL
and df1 is like so:
ID Letter
1 A
2 B
3 C
I want to merge the two dataframes so that each name is assigned a different letter each time. So the result should be like this:
Week Name Letter
1 TOM A
1 BEN B
1 CARL C
2 TOM B
2 BEN C
2 CARL A
3 TOM C
3 BEN A
3 CARL B
Any help would be greatly appreciated. Thanks in advance.
df1['Letter'] = df1.groupby('Week').cumcount().add(df1['Week'].sub(1)).mod(df1.groupby('Week').transform('count')['Name']).map(df2['Letter'])
Output:
>>> df1
Week Name Letter
0 1 TOM A
1 1 BEN B
2 1 CARL C
3 2 TOM B
4 2 BEN C
5 2 CARL A
6 3 TOM C
7 3 BEN A
8 3 CARL B
I have a pandas dataframe which consists of players names and statistics from a sporting match. The only source of data lists them in the following format:
# PLAYER M FG 3PT FT REB AST STL PTS
34 BLAKE Brad 38 17 5 6 3 0 3 0 24
12 JONES Ben 42 10 2 6 1 0 4 1 12
8 SMITH Todd J. 16 9 1 4 1 0 3 2 18
5 MAY-DOUGLAS James 9 9 0 3 1 0 2 1 6
44 EDLIN Taylor 12 6 0 5 1 0 0 1 8
The players names are in reverse order: Surname Firstname. I need to transform the names to the current order of firstname lastname. So, specifically:
BLAKE Brad -> Brad BLAKE
SMITH Todd J. -> Todd J. SMITH
MAY-DOUGLAS James -> James MAY-DOUGLAS
The case of the letters do not matter, however I thought potentially they could be used to differentiate the first and lastname. I know all lastnames with always be in uppercase even if they include a hyphen. The first name will always be sentence case (first letter uppercase and the rest lowercase). However some names include the middle name to differentiate players with the same name. I see how a space character can be used a delemiter and potentially use a "split" transformation but it guess difficult with the middle name character.
Is there any suggestions of a function from Pandas I can use to achieve this?
The desired out put is:
# PLAYER M FG 3PT FT REB AST STL PTS
34 Brad BLAKE 38 17 5 6 3 0 3 0 24
12 Ben JONES 42 10 2 6 1 0 4 1 12
8 Todd J. SMITH 16 9 1 4 1 0 3 2 18
5 James MAY-DOUGLAS 9 9 0 3 1 0 2 1 6
44 Taylor EDLIN 12 6 0 5 1 0 0 1 8
Try to split by first whitespace, then reverse the list and join list values with whitespace.
df['PLAYER'] = df['PLAYER'].str.split(' ', 1).str[::-1].str.join(' '))
To reverse only certain names, you can use isin then boolean indexing
names = ['BLAKE Brad', 'SMITH Todd J.', 'MAY-DOUGLAS James']
mask = df['PLAYER'].isin(names)
df.loc[mask, 'PLAYER'] = df.loc[mask, 'PLAYER'].str.split('-', 1).str[::-1].str.join(' ')
Given the pandas dataframe as follows:
Partner1 Partner2 Interactions
0 Ann Alice 1
1 Alice Kate 8
2 Kate Tony 9
3 Tony Ann 2
How can I group by a specific partner, let's say to find the total number of interactions of Ann?
Something like
gb = df.groupby(['Partner1'] or ['Partner2']).agg({'Interactions': 'sum'})
and getting the answer:
Partner Interactions
Ann 3
Alice 9
Kate 17
Tony 11
You can use melt together with groupby. First melt:
df = pd.melt(df, id_vars='Interactions', value_vars=['Partner1', 'Partner2'], value_name='Partner')
This will give:
Interactions variable Partner
0 1 Partner1 Ann
1 8 Partner1 Alice
2 9 Partner1 Kate
3 2 Partner1 Tony
4 1 Partner2 Alice
5 8 Partner2 Kate
6 9 Partner2 Tony
7 2 Partner2 Ann
Now, group by Partner and sum:
df.groupby('Partner')[['Interactions']].sum()
Result:
Partner Interactions
Alice 9
Ann 3
Kate 17
Tony 11
You can do merge dataframe itself:
# join the df to itself
join_df = df.merge(df, left_on='Partner1', right_on='Partner2', suffixes=('', '_'))
# get sum
join_df['InteractionsSum'] = join_df[['Interactions', 'Interactions_']].agg(sum, 1)
join_df = join_df[['Partner1', 'Interactions']].copy()
print(join_df)
Partner1 Interactions
0 Ann 1
1 Alice 8
2 Kate 9
3 Tony 2
I'm having trouble with some pandas groupby object issue, which is the following:
so I have this dataframe:
Letter name num_exercises
A carl 1
A Lenna 2
A Harry 3
A Joe 4
B Carl 5
B Lenna 3
B Harry 3
B Joe 6
C Carl 6
C Lenna 3
C Harry 4
C Joe 7
And I want to add a column on it, called num_exercises_total , which contains the total sum of num_exercises for each letter. Please note that this value must be repeated for each row in the letter group.
The output would be as follows:
Letter name num_exercises num_exercises_total
A carl 1 15
A Lenna 2 15
A Harry 3 15
A Joe 4 15
B Carl 5 18
B Lenna 3 18
B Harry 3 18
B Joe 6 18
C Carl 6 20
C Lenna 3 20
C Harry 4 20
C Joe 7 20
I've tried adding the new column like this:
df['num_exercises_total'] = df.groupby(['letter'])['num_exercises'].sum()
But it returns the value NaN for all the rows.
Any help would be highly appreciated.
Thank you very much in advance!
You may want to check transform
df.groupby(['Letter'])['num_exercises'].transform('sum')
0 10
1 10
2 10
3 10
4 17
5 17
6 17
7 17
8 20
9 20
10 20
11 20
Name: num_exercises, dtype: int64
df['num_of_total']=df.groupby(['Letter'])['num_exercises'].transform('sum')
Transform works perfectly for this question. WenYoBen is right. I am just putting slightly different version here.
df['num_of_total']=df['num_excercises'].groupby(df['Letter']).transform('sum')
>>> df
Letter name num_excercises num_of_total
0 A carl 1 10
1 A Lenna 2 10
2 A Harry 3 10
3 A Joe 4 10
4 B Carl 5 17
5 B Lenna 3 17
6 B Harry 3 17
7 B Joe 6 17
8 C Carl 6 20
9 C Lenna 3 20
10 C Harry 4 20
11 C Joe 7 20
I have a df as below:
Index Site Name
0 Site_1 Tom
1 Site_2 Tom
2 Site_4 Jack
3 Site_8 Rose
5 Site_11 Marrie
6 Site_12 Marrie
7 Site_21 Jacob
8 Site_34 Jacob
I would like to strip the 'Site_' and only leave the number in the "Site" column, as shown below:
Index Site Name
0 1 Tom
1 2 Tom
2 4 Jack
3 8 Rose
5 11 Marrie
6 12 Marrie
7 21 Jacob
8 34 Jacob
What is the best way to do this operation?
Using pd.Series.str.extract
This produces a copy with an updated columns
df.assign(Site=df.Site.str.extract('\D+(\d+)', expand=False))
Site Name
Index
0 1 Tom
1 2 Tom
2 4 Jack
3 8 Rose
5 11 Marrie
6 12 Marrie
7 21 Jacob
8 34 Jacob
To persist the results, reassign to the data frame name
df = df.assign(Site=df.Site.str.extract('\D+(\d+)', expand=False))
Using pd.Series.str.split
df.assign(Site=df.Site.str.split('_', 1).str[1])
Alternative
Update instead of producing a copy
df.update(df.Site.str.extract('\D+(\d+)', expand=False))
# Or
# df.update(df.Site.str.split('_', 1).str[1])
df
Site Name
Index
0 1 Tom
1 2 Tom
2 4 Jack
3 8 Rose
5 11 Marrie
6 12 Marrie
7 21 Jacob
8 34 Jacob
Make a array consist of the names you want. Then call
yourarray = pd.DataFrame(yourpd, columns=yournamearray)
Just call replace on the column to replace all instances of "Site_":
df['Site'] = df['Site'].str.replace('Site_', '')
Use .apply() to apply a function to each element in a series:
df['Site Name'] = df['Site Name'].apply(lambda x: x.split('_')[-1])
You can use exactly what you wanted (the strip method)
>>> df["Site"] = df.Site.str.strip("Site_")
Output
Index Site Name
0 1 Tom
1 2 Tom
2 4 Jack
3 8 Rose
5 11 Marrie
6 12 Marrie
7 21 Jacob
8 34 Jacob