python从excel中读取数据 写入word_在python3中从excel到word的数据传输
只是为了展示您使用pandas的示例性功能：import pandas as pd
df = pd.read_csv('whereverfilemayroam/filename')
Participant ID Breakfasts Lunches/dinners Snacks
0 1111 Full english Risotto Granola
1 1111 Full english Risotto Granola
2 1111 Full english Risotto Granola
3 1111 Full english Risotto Granola
4 1111 Full english Risotto Granola
5 1111 Full english Risotto Granola
6 1111 Full english Risotto Granola
7 1111 None Risotto Granola
8 1111 None Risotto Granola
9 1111 None Risotto Granola
10 1111 None Risotto Granola
11 1111 None Risotto Granola
12 1111 None Risotto Granola
13 1111 None Risotto Granola
14 2222 Avocado toast Bean chilli Apple
15 2222 Avocado toast Bean chilli Apple
16 2222 Avocado toast Bean chilli Apple
17 2222 Avocado toast Bean chilli Apple
18 2222 Avocado toast Bean chilli Apple
19 2222 Avocado toast Bean chilli Apple
20 2222 Avocado toast Bean chilli Apple
21 2222 None Bean chilli Apple
22 2222 None Bean chilli Apple
23 2222 None Bean chilli Apple
24 2222 None Bean chilli Apple
25 2222 None Bean chilli Apple
26 2222 None Bean chilli Apple
27 2222 None Bean chilli Apple
这是pandas数据帧中的文件，pandas中的标准容器，如果您愿意的话。现在你可以用它做大量的统计数据。。。只需在docs中挖掘一点
示例：
^{pr2}$
当然，您可以按参与者ID分开：oneoneoneone = df[df['Participant ID'] == 1111]
oneoneoneone
Participant ID Breakfasts Lunches/dinners Snacks
0 1111 Full english Risotto Granola
1 1111 Full english Risotto Granola
2 1111 Full english Risotto Granola
3 1111 Full english Risotto Granola
4 1111 Full english Risotto Granola
5 1111 Full english Risotto Granola
6 1111 Full english Risotto Granola
7 1111 None Risotto Granola
8 1111 None Risotto Granola
9 1111 None Risotto Granola
10 1111 None Risotto Granola
11 1111 None Risotto Granola
12 1111 None Risotto Granola
13 1111 None Risotto Granola
oneoneoneone.to_csv('target_file')
也许也是twotwotwotwo.to_csv('another_target_file')
也可以迭代组，然后对每个组应用相同的操作。
e、 g.：for name, group in df.groupby('Participant ID'):
print(name)
print(group.groupby('Breakfasts').Breakfasts.count().to_string())
print(group.groupby('Lunches/dinners')['Lunches/dinners'].count().to_string())
print(group.groupby('Snacks').Snacks.count().to_string(), '\n')
退货：1111
Breakfasts
Full english 7
Lunches/dinners
Risotto 14
Snacks
Granola 14
2222
Breakfasts
Avocado toast 7
Lunches/dinners
Bean chilli 14
Snacks
Apple 14