import pandas as pd
df = pd.DataFrame({"k":["a","a","b"],"v":[1,2,3]})
print(df.groupby("k", as_index=False).agg(total=("v","sum")))
    

   k  total
0  a      3
1  b      3
    

orders = pd.DataFrame({
  "cust":["c1","c1","c2","c2","c2"],
  "amt":[10,40,5,8,20]
})
out = orders.groupby("cust", as_index=False).agg(
  orders=("amt","count"),
  revenue=("amt","sum"),
  p90=("amt", lambda s: s.quantile(0.9))
)
print(out)
    

  cust  orders  revenue   p90
0   c1       2       50  37.0
1   c2       3       33  17.6
    

g = orders.groupby("cust")["amt"]
orders["share"] = orders["amt"] / g.transform("sum")
orders["z"] = (orders["amt"] - g.transform("mean")) / g.transform("std")
print(orders.head())
    

  cust  amt     share         z
0   c1   10  0.200000 -1.000000
1   c1   40  0.800000  1.000000
2   c2    5  0.119048 -0.773131
...
    

def topk(d, k=2): 
    return d.nlargest(k, "amt")[["cust","amt"]]
print(orders.groupby("cust", group_keys=False).apply(topk, k=1))
    

  cust  amt
1   c1   40
4   c2   20
    

import numpy as np
orders["seg"] = np.where(orders["cust"]=="c1","high","regular")
pt = orders.pivot_table(index="seg", columns="cust", values="amt", aggfunc="sum", margins=True)
print(pt.fillna(0))
    

cust        c1    c2   All
seg                      
high      50.0   0.0  50.0
regular    0.0  33.0  33.0
All       50.0  33.0  83.0
    

import pandas as pd
ts = pd.date_range("2024-01-01", periods=7, freq="D")
x = pd.Series([1,3,2,4,5,2,1], index=ts)
print(x.resample("D").sum().head(3))
    

2024-01-01    1
2024-01-02    3
2024-01-03    2
Freq: D, dtype: int64
    

roll7 = x.rolling(3, min_periods=1).mean()
print(pd.DataFrame({"x": x, "roll3": roll7}).head(5))
    

             x     roll3
2024-01-01  1  1.000000
2024-01-02  3  2.000000
2024-01-03  2  2.000000
2024-01-04  4  3.000000
2024-01-05  5  3.666667