如何使用一个函数两次?

Question

我必须使用相同的函数两次。参数为df时为第一次，参数为df3时为第二次。怎么做呢?功能:

def add(df, df3):
df["timestamp"] = pd.to_datetime(df["timestamp"])
df = df.groupby(pd.Grouper(key = "timestamp", freq = "h")).agg("mean")
price = df["price"]
amount = df["amount"]
return (price * amount) // amount

双重用途:

out = []
# This loop will use the add(df) function for every csv and append in a list
for f in csv_files:
df = pd.read_csv(f, header=0)
# Replace empty values with numpy, not sure if usefull,  maybe pandas can handle this
df.replace("", np.nan)  
#added aggregate DataFrame with new column to list of DataFrames
out.append(add(df))
out2 = []
df3 = pd.Series(dtype=np.float64)
for f in csv_files:
df2 = pd.read_csv(f, header=0)
df3 = pd.concat([df3, df2], ignore_index=True)
out2 = pd.DataFrame(add(df = df3))
out2

我得到了错误:

TypeError: add() missing 1 required positional argument: 'df3'

Answer 1

add函数的名称与脚本其余部分的df和df3变量名称无关。

正如@garagnoth所说，在add中只需要一个参数。你可以叫它df、foo或myvariablename，它与df和df3都没有关系。

在您的情况下，您可以将add函数更改为以下内容:

def add(a_dataframe):
# I set the argument name to "a_dataframe" so you can
# see its name is not linked to outside variables
a_dataframe["timestamp"] = pd.to_datetime(a_dataframe["timestamp"])
a_dataframe = a_dataframe.groupby(pd.Grouper(key = "timestamp", freq = "h")).agg("mean")
price = a_dataframe["price"]
amount = a_dataframe["amount"]
return (price * amount) // amount

您现在可以使用df或df3调用此函数，就像脚本的其余部分一样。