Why the column name is missing in pandas output in the group by result? - pandas

Question

Update
if use to_frame() the column name seems not in the same row
重量
型号
HG-R2075 2040
HG220 680
This is my code, it groups the "型号"(which means type), and get the sum of the "重量"(weight) and exclude the column("是否发送") with a value in it.
import pandas as pd
import numpy as np
import sys
import os
script_dir = os.path.dirname(os.path.abspath(__file__))
os.chdir(script_dir ) # change to the path that you already know
try:
ClientName = sys.argv[1]
except :
print(u'没有输入或者错误的客户名称!')
df = pd.read_excel("Summary.xlsm")
df = df[df['客户'].str.contains(ClientName)][pd.isnull(df[u"是否已经发送"])].groupby([ u'型号'])[u'重量'].sum()
print('[CQ:face,id=21] ' + '*' * 10 + u'以下是' + ClientName + u'未发送的重量' + '*' * 10 + '[CQ:face,id=21]')
print(str(df))
Output is this :
[CQ:face,id=21] **********以下是KATUN未发送的重量**********[CQ:face,id=
21]
型号 (****the column name is missing here*****)
HG-R2075 2040
HG220 680
Name: 重量, dtype: int64
I don't know why the column name is missing?
The output I want is this: how to make it?
型号 重量
HG-R2075 2040
HG220 680
Name: 重量, dtype: int64

Answer 1

The result df of your groupby operation is actually a Series, not a DataFrame. That's why it is printed with a different format.
print(df.to_frame()) should to the trick.
EDIT: Actually in such a dataframe index name and column name will not be printed on the same row. To get a cleaner output, use reset_index to get 2 proper columns:
print(df.reset_index().to_string(index=False))

Answer 2

First use boolean indexing with chaining by &.
If need 2 column DataFrame add as_index=False or Series.reset_index:
mask = df['客户'].str.contains(ClientName) & df[u"是否已经发送"].isnull()
df = df[mask].groupby([ u'型号'], as_index=False)[u'重量'].sum()
Or:
df = df[mask].groupby([ u'型号'])[u'重量'].sum().reset_index()
For one column DataFrame use Series.to_frame - first column is index:
df = df[mask].groupby([ u'型号'])[u'重量'].sum().to_frame()
Sample:
np.random.seed(345)
N = 10
df = pd.DataFrame({'客户':np.random.choice(list('abc'), size=N),
u"是否已经发送":np.random.choice([np.nan,0], size=N),
u'型号':np.random.randint(2, size=N),
u'重量':np.random.randint(10, size=N)})
print (df)
型号 客户 是否已经发送 重量
0 0 a 0.0 4
1 0 a 0.0 0
2 1 b NaN 8
3 1 b NaN 5
4 1 c 0.0 6
5 1 a NaN 3
6 1 a NaN 3
7 1 b 0.0 4
8 0 a NaN 2
9 1 c NaN 8
ClientName = 'a'
mask = df['客户'].str.contains(ClientName) & df[u"是否已经发送"].isnull()
df1 = df[mask].groupby([ u'型号'], as_index=False)[u'重量'].sum()
print(df1)
型号 重量
0 0 2
1 1 6
df1 = df[mask].groupby([ u'型号'])[u'重量'].sum().reset_index()
print(df1)
型号 重量
0 0 2
1 1 6
df2 = df[mask].groupby([ u'型号'])[u'重量'].sum().to_frame()
print (df2)
重量
型号
0 2
1 6