下面是一个使用pandas.DataFrame.groupby和pandas.DataFrame.apply的命题：

#Extract the month number from the start date and convert it to a month name
df["Month"]= pd.to_datetime(df["Timerange"].str.extract(r"(\d+)/\d+", expand=False), format="%m").dt.month_name()

#Convert comma separated strings to numbers
df["Dials"]= df["Dials"].str.replace(",", "").astype(float)

out = (
        df.groupby("Month", as_index=False)
                .apply(lambda x: pd.Series({"Dials": x["Dials"].sum(),
                                            "Conn": x["Conn"].sum(),
                                            "Dials -> Conn": x["Conn"].sum()/x["Dials"].sum(),
                                            "Dials -> Conv": x["Conv"].sum()/x["Dials"].sum()}))

      )

#输出：

print(out)

     Month   Dials   Conn  Dials -> Conn  Dials -> Conv
0  October  6643.0  291.0       0.043806       0.026645

我假设你的时间范围总是从你感兴趣的月份开始，并且所有数据都来自同一年（今年）。如果这些假设是合理的，那么这是可行的：

emps = [
    "Ricky Ponting", "Adam Gilchrist", "Michael Clarke", "Shane Warne"
]

timeranges = [
    "10/3 - 10/7", "10/3 - 10/7", "10/3 - 10/7", "10/3 - 10/7"
]

dials = [1869, 1336, 1960, 1478]
conn = [102, 53, 74, 62]
conv = [60, 30, 42, 45]

import pandas as pd

df = pd.DataFrame(
    {
        "Employee": emps,
        "Timerange": timeranges,
        "Dials": dials,
        "Conn": conn,
        "Conv": conv
    }
)

import datetime

def get_month(row):
    month = int(row["Timerange"].split("/")[0])
    return datetime.date(year=2022, month=month, day=1).strftime("%B")

df["Month"] = df.apply(get_month, axis=1)

sums = df.groupby("Month").sum()
sums["Dials -> Conn"] = sums["Conn"] / sums["Dials"]
sums["Dials -> Conv"] = sums["Conv"] / sums["Dials"]
sums