I'm starting with a dataframe of baseabll seasons a section of which looks similar to this:
Name Season AB H SB playerid
13047 A.J. Pierzynski 2013 503 137 1 746
6891 A.J. Pierzynski 2006 509 150 1 746
1374 Rod Carew 1977 616 239 23 1001942
1422 Stan Musial 1948 611 230 7 1009405
1507 Todd Helton 2000 580 216 5 432
1508 Nomar Garciaparra 2000 529 197 5 190
1509 Ichiro Suzuki 2004 704 262 36 1101
From these seasons, I want to create a dataframe of career stats; that is, one row for each player which is a sum of their AB, H, etc. This dataframe should still include the names of the players. The playerid in the above is a unique key for each player and should either be an index or an unchanged value in a column after creating the career stats dataframe.
My hypothetical starting point is df_careers = df_seasons.groupby('playerid').agg(sum) but this leaves out all the non-numeric data. With numeric_only = False I can get some sort of mess in the names columns like 'Ichiro SuzukiIchiro SuzukiIchiro Suzuki' from concatenation, but that just requires a bunch of cleaning. This is something I'd like to be able to do with other data sets and the actually data I have is more like 25 columns, so I'd rather understand a specific routine for getting the Name data back or preserving it from the outset rather than write a specific function and use groupby('playerid').agg(func) (or a similar process) to do it, if possible.
I'm guessing there's a fairly simply way to do this, but I only started learning Pandas a week ago, so there are gaps in my knowledge.
You can write your own condition how do you want to include non summed columns.
col = df.columns.tolist()
col.remove('playerid')
df.groupby('playerid').agg({i : lambda x: x.iloc[0] if x.dtypes=='object' else x.sum() for i in df.columns})
df:
Name Season AB H SB playerid
playerid
190 Nomar_Garciaparra 2000 529 197 5 190
432 Todd_Helton 2000 580 216 5 432
746 A.J._Pierzynski 4019 1012 287 2 1492
1101 Ichiro_Suzuki 2004 704 262 36 1101
1001942 Rod_Carew 1977 616 239 23 1001942
1009405 Stan_Musial 1948 611 230 7 1009405
If there is a one-to-one relationship between 'playerid' and 'Name', as appears to be the case, you can just include 'Name' in the groupby columns:
stat_cols = ['AB', 'H', 'SB']
groupby_cols = ['playerid', 'Name']
results = df.groupby(groupby_cols)[stat_cols].sum()
Results:
AB H SB
playerid Name
190 Nomar Garciaparra 529 197 5
432 Todd Helton 580 216 5
746 A.J. Pierzynski 1012 287 2
1101 Ichiro Suzuki 704 262 36
1001942 Rod Carew 616 239 23
1009405 Stan Musial 611 230 7
If you'd prefer to group only by 'playerid' and add the 'Name' data back in afterwards, you can instead create a 'playerId' to 'Name' mapping as a dictionary, and look it up using map:
results = df.groupby('playerid')[stat_cols].sum()
name_map = pd.Series(df.Name.to_numpy(), df.playerid).to_dict()
results['Name'] = results.index.map(name_map)
Results:
AB H SB Name
playerid
190 529 197 5 Nomar Garciaparra
432 580 216 5 Todd Helton
746 1012 287 2 A.J. Pierzynski
1101 704 262 36 Ichiro Suzuki
1001942 616 239 23 Rod Carew
1009405 611 230 7 Stan Musial
groupy.agg() can accept a dictionary that maps column names to functions. So, one solution is to pass a dictionary to agg, specifying which functions to apply to each column.
Using the sample data above, one might use
mapping = { 'AB': sum,'H': sum, 'SB': sum, 'Season': max, 'Name': max }
df_1 = df.groupby('playerid').agg(mapping)
The choice to use 'max' for those that shouldn't be summed is arbitrary. You could define a lambda function to apply to a column if you want to handle it in a certain way. DataFrameGroupBy.agg can work with any function that will work with DataFrame.apply.
To expand this to larger data sets, you might use a dictionary comprehension. This would work well:
dictionary = { x : sum for x in df.columns}
dont_sum = {'Name': max, 'Season': max}
dictionary.update(dont_sum)
df_1 = df.groupby('playerid').agg(dictionary)
Say I have the following data frame,
df.head()
ID start end symbol symbol_id type
1 146 291 bus bus-201 CDS
1 146 314 bus bus-201 trans
1 603 243 bus bus-201 CDS
1 1058 2123 car car-203 CDS
1 910 81 car car-203 ex
1 2623 2686 car car-203 CDS
1 5948 6043 car car-203 CDS
1 6348 6474 car car-203 CDS
1 910 81 car car-201 ex
1 910 81 car car-201 ex
1 636 650 car car-203 CDS
1 202 790 train train-204 CDS
1 200 314 train train-204 CDS
1 202 837 train train-204 CDS
Now from the above data frame, I need to group by items based on column symbol_id if column type is CDS. Then, I need to use the first value from the column start as the value in start column of the new data frame and last value from columnendas the value in columnend`.
Finally, the df2 should look like,
start end symbol symbol_id type
146 243 bus bus-203 CDS
1058 650 car car-203 CDS
202 837 train train-204 CDS
I have tried using list of values from df['symbol'],
sym_list=df['symbol'].tolist().drop_duplicates()
for symbol in df['symbol'].values:
if symbol in tuple(sym_list):
df_symbol =df['symbol'].isin(symbol)
which threw the following error,
TypeError: only list-like objects are allowed to be passed to isin(), you passed a [str]
I was trying to capture the first and last value for each symbol and symbol_id value using,
start = df.query('type =="CDS"')[['start']].iloc[0]
end = df.query('type =="CDS"')[['end']].iloc[-1]
However, my data frame is quite big and I have more than 50,000 unique values for symbol, hence I need a better solution here.
Any help or suggestions are appreciated!!
you can do it using group by and first and last aggrigate function
df[df["type"]=="CDS"].groupby("symbol_id").agg({"start":"first", "end":"last", "symbol":"first","symbol_id":"first", "type":"first"})
Try:
df_group = df[df['type']=='CDS'].groupby(['symbol_id', 'symbol', 'type'])
df_new = pd.DataFrame(columns =['start', 'end'])
df_new[['start', 'end']] = df_group.agg({'start':'first', 'end': 'last'})
df_new.reset_index()
symbol_id symbol start end type
0 bus-201 bus 146 243 CDS
1 car-203 car 1058 650 CDS
2 train-204 train 202 837 CDS
Edited using agg as used by #Dev Khadka
I am looking for a way to aggregate over a large dataframe, possibly using groupby. Each group would be based on either pre-specified columns or regex, and the aggregation should produce a named output.
This produces a sample dataframe:
import pandas as pd
import itertools
import numpy as np
col = "A,B,C".split(',')
col1 = "1,2,3,4,5,6,7,8,9".split(',')
col2 = "E,F,G".split(',')
all_dims = [col, col1, col2]
all_keys = ['.'.join(i) for i in itertools.product(*all_dims)]
rng = pd.date_range(end=pd.Timestamp.today().date(), periods=12, freq='M')
df = pd.DataFrame(np.random.randint(0, 1000, size=(len(rng), len(all_keys))), columns=all_keys, index=rng)
Above produces a dataframe with one year's worth of monthly data, with 36 columns with following names:
['A.1.E', 'A.1.F', 'A.1.G', 'A.2.E', 'A.2.F', 'A.2.G', 'A.3.E', 'A.3.F',
'A.3.G', 'A.4.E', 'A.4.F', 'A.4.G', 'A.5.E', 'A.5.F', 'A.5.G', 'A.6.E',
'A.6.F', 'A.6.G', 'A.7.E', 'A.7.F', 'A.7.G', 'A.8.E', 'A.8.F', 'A.8.G',
'A.9.E', 'A.9.F', 'A.9.G', 'B.1.E', 'B.1.F', 'B.1.G', 'B.2.E', 'B.2.F',
'B.2.G', 'B.3.E', 'B.3.F', 'B.3.G', 'B.4.E', 'B.4.F', 'B.4.G', 'B.5.E',
'B.5.F', 'B.5.G', 'B.6.E', 'B.6.F', 'B.6.G', 'B.7.E', 'B.7.F', 'B.7.G',
'B.8.E', 'B.8.F', 'B.8.G', 'B.9.E', 'B.9.F', 'B.9.G', 'C.1.E', 'C.1.F',
'C.1.G', 'C.2.E', 'C.2.F', 'C.2.G', 'C.3.E', 'C.3.F', 'C.3.G', 'C.4.E',
'C.4.F', 'C.4.G', 'C.5.E', 'C.5.F', 'C.5.G', 'C.6.E', 'C.6.F', 'C.6.G',
'C.7.E', 'C.7.F', 'C.7.G', 'C.8.E', 'C.8.F', 'C.8.G', 'C.9.E', 'C.9.F',
'C.9.G']
What I would like now is to be able aggregate over the dataframe and take certain column combinations and produce named outputs. For example, one rules might be that I will take all 'A.*.E' columns (that have any number in the middle), sum them and produce a named output column called 'A.SUM.E'. And then do the same for 'A.*.F', 'A.*.G' and so on.
I have looked into pandas 25 named aggregation which allows me to name my outputs but I couldn't see how to simultaneously capture the right column combinations and produce the right output names.
If you need to reshape the dataframe to make a workable solution, that is fine as well.
Note, I am aware I could do something like this in a Python loop but I am looking for a pandas way to do it.
Not a groupby solution and it uses a loop but I think it's nontheless rather elegant: first get a list of unique column from - to combinations using a set and then do the sums using filter:
cols = sorted([(x[0],x[1]) for x in set([(x.split('.')[0], x.split('.')[-1]) for x in df.columns])])
for c0, c1 in cols:
df[f'{c0}.SUM.{c1}'] = df.filter(regex = f'{c0}\.\d+\.{c1}').sum(axis=1)
Result:
A.1.E A.1.F A.1.G A.2.E ... B.SUM.G C.SUM.E C.SUM.F C.SUM.G
2018-08-31 978 746 408 109 ... 4061 5413 4102 4908
2018-09-30 923 649 488 447 ... 5585 3634 3857 4228
2018-10-31 911 359 897 425 ... 5039 2961 5246 4126
2018-11-30 77 479 536 509 ... 4634 4325 2975 4249
2018-12-31 608 995 114 603 ... 5377 5277 4509 3499
2019-01-31 138 612 363 218 ... 4514 5088 4599 4835
2019-02-28 994 148 933 990 ... 3907 4310 3906 3552
2019-03-31 950 931 209 915 ... 4354 5877 4677 5557
2019-04-30 255 168 357 800 ... 5267 5200 3689 5001
2019-05-31 593 594 824 986 ... 4221 2108 4636 3606
2019-06-30 975 396 919 242 ... 3841 4787 4556 3141
2019-07-31 350 312 104 113 ... 4071 5073 4829 3717
If you want to have the result in a new DataFrame, just create an empty one and add the columns to it:
result = pd.DataFrame()
for c0, c1 in cols:
result[f'{c0}.SUM.{c1}'] = df.filter(regex = f'{c0}\.\d+\.{c1}').sum(axis=1)
Update: using simple groupby (which is even more simple in this particular case):
def grouper(col):
c = col.split('.')
return f'{c[0]}.SUM.{c[-1]}'
df.groupby(grouper, axis=1).sum()
I have a variable in the following format fg = 2017-20. It's a string. And also I have a dataframe:
flag №
2017-18 389
2017-19 390
2017-20 391
2017-21 392
2017-22 393
2017-23 394
...
I need to find this value (fg) in the column "flag" and select the appropriate value (in the example it will be 391) in the column "№". Then create new dataframe, in which there will also be a column "№". Add this value to this dataframe and iterate 53 times. The result should look like this:
№_new
391
392
393
394
395
...
442
443
444
It does not look difficult, but I can not find anything suitable based on other issues. Can someone advise anything, please?
You need boolean indexing with loc for filtering, then convert one item Series to scalar by convert to numpy array by values and select first value by [0].
Last create new DataFrame with numpy.arange.
fg = '2017-20'
val = df.loc[df['flag'] == fg, '№'].values[0]
print (val)
391
df1 = pd.DataFrame({'№_new':np.arange(val, val+53)})
print (df1)
№_new
0 391
1 392
2 393
3 394
4 395
5 396
6 397
7 398
8 399
9 400
10 401
11 402
..
..
I have a data frame like below:
i_id q_id
month category_bucket
Aug Algebra Tutoring 187 64
Balloon Artistry 459 401
Carpet Installation or Replacement 427 243
Dance Lessons 181 46
Landscaping 166 60
Others 9344 4987
Tennis Instruction 161 61
Tree and Shrub Service 383 269
Wedding Photography 161 49
Window Repair 140 80
Wiring 439 206
July Algebra Tutoring 555 222
Balloon Artistry 229 202
Carpet Installation or Replacement 140 106
Dance Lessons 354 115
Landscaping 511 243
Others 9019 4470
Tennis Instruction 613 324
Tree and Shrub Service 130 100
Wedding Photography 425 191
Window Repair 444 282
Wiring 154 98
It's a multi-index data frame with month and category bucket as index. And i_id, q_id as columns
I got this by doing a groupby operation on a normal data frame like below
invites_combined.groupby(['month', 'category_bucket'])[["i_id","q_id"]].count()
I basically want a data frame where I have 4 columns 2 each for i_id, q-Id for both the months and a column for category_bucket. So basically converting the above multi-index data frame to single index so that I can access the values.
Currently it's difficult for me to access the values of i_id, q_id along for a particular month and category value.
If you feel there is an easier way to access the i_id and q_id values for each category and month without having to convert to single index that is fine too.
Single index would be easier to loop into each value for each combination of month and category though.
It seems you need reset_index for convert MultiIndex to columns:
df = df.reset_index()