使用数学计算从现有数据帧创建新数据帧

Question

我有 2 个数据框，如下所示：

monthly_abscount = pd.DataFrame(df.groupby(df["Month"])["Abscount"].sum())
monthly_abscount["Abscount"] = monthly_abscount["Abscount"].astype(float) 
monthly_abscount.head()

    Abscount
Month   
1   264.0
2   163.0
3   283.0
4   226.0
5   66.0

月份列是这里的索引。

workdays = workdays2 #creating new DF for working
workdays2["Month"]= workdays2["Month"].replace(['Jan 2022','Feb 2022','Mar 2022','Apr 2022','May 2022','Jun 2022','Jul 2022','Aug 2022','Sep 2022','Oct 2022','Nov 2022','Dec 2022'],['1','2','3','4','5','6','7','8','9','10','11','12'])
workdays2["Month"] = workdays2["Month"].astype(str)
workdays2["Workdays"] = workdays2["Workdays"].astype(float)
workdays2.head()

    Month   Workdays
0   1.0 21.0
1   2.0 20.0
2   3.0 23.0
3   4.0 21.0
4   5.0 22.0

我想合并它们以最终计算 Abscount/Workdays 以获得每个月的 Abscount 平均值，或者我想直接创建一个包含列 Abscount/Workdays 的数据框。

这是两个数据帧的信息：

workdays2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 2 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Month     5 non-null      object 
 1   Workdays  5 non-null      float64
dtypes: float64(1), object(1)
memory usage: 208.0+ bytes

monthly_abscount.info()

<class 'pandas.core.frame.DataFrame'>
Index: 5 entries, 1 to 5
Data columns (total 1 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Abscount  5 non-null      float64
dtypes: float64(1)
memory usage: 80.0+ bytes

这是我目前试过的代码：

mergedpd = pd.merge(workdays2, monthly_abscount, on = 'Month', how = "inner")
print(mergedpd)

Empty DataFrame
Columns: [Month, Workdays, Abscount]
Index: []

以上代码给出了一个空数据框。

我在这里做错了什么？请帮忙。

Answer 1

使用：

#The reason of empty result.
df1=df1.reset_index()
#######

df2['Month']=df2['Month'].astype(float).astype(int)
df1['Month']=df1['Month'].astype(int)
out = df1.merge(df2, on='Month')
out['Abscount']=out['Abscount'].astype(float)
out['Workdays']=out['Workdays'].astype(float)
out['needed'] = out['Abscount']/out['Workdays']

输出：

index   Month   Abscount    Workdays    needed
0   0   1   264.0   21.0    12.571429
1   1   2   163.0   20.0    8.150000
2   2   3   283.0   23.0    12.304348
3   3   4   226.0   21.0    10.761905
4   4   5   66.0    22.0    3.000000

使用数学计算从现有数据帧创建新数据帧

Creating new dataframe from existing dataframes using a mathematical calculation

python

merge

pandas