来自excel文件的python字典列表

Question

Hey guys so I'm making a dictionary file that has a table name and column name and table name is repeated many times for how many column names there are in the excel file. So for example

 | table_name| column_name|
 | ----------|------------|
 | players   |  name      |
 | players   |  height    |
 | players   |  age       |
 | teams     | name       |
 | teams     | city       |
 | teams     | owner      |

and it goes down and down. I have around 1000 rows here, so I could type them up in the format that I desire but feels like it would take a lot of time. Here is the format that I'm trying to get in a list of dictionaries.

[{'players':['name', 'height', 'age']}, {'teams':['name', 'city', 'owner']}, ....]

Answer 1

One option can be to read an excel file with pandas.

You can use pandas.DataFrame.groupby() then get the result of groupby as list with apply . At then end use pandas.Series.to_dict() .

import pandas as pd
file_path = "Book1.xlsx"
df = pd.read_excel(file_path)
# >>> df
#   table_name column_name
# 0    players        name
# 1    players      height
# 2    players         age
# 3      teams        name
# 4      teams        city
# 5      teams       owner

dct = df.groupby('table_name')['column_name'].apply(list).to_dict()

# dct -> {'players': ['name', 'height', 'age'], 'teams': ['name', 'city', 'owner']}

# For converting the above 'dict', you can use the below 'list comprehension':
lst_dct = [{k:v} for k,v in dct.items()]

print(lst_dct)

Output:

[{'players': ['name', 'height', 'age']}, {'teams': ['name', 'city', 'owner']}]

Answer 2

ahh thanks I'mahdi, I actually didn't see your answer, and my answer is actually pretty close to yours, just posting it just in case there is a need for a dictionary of all tables minus the list. but just saw you also included that as well:). Glad we came to the same conclusion here that pandas is a nice library to use.

import pandas

def excel_to_dict():
   csvFile = pandas.read_csv('qv_columns.csv')
   tables_grouped = csvFile.groupby('TABLE_NAME'). 
   ['COLUMN_NAME'].agg(list)
   tables_dict = tables_grouped.to_dict()
   print(tables_dict)