遍历字典并为变量赋值

Question

I have a dictionary "d" which has a 10 keys with pyspark dataframes as values.

 >> d.keys()
  dict_keys (['Py1', 'Py2', 'Py3', 'Py4', 'Py7', 'Py8', 'Py15', 'Py20', 'Py21', 'Py22']

I am currently taking each key and its value, then assigning it to a variable like so:

   df1 = d['Py1'] 
   df2 = d['Py2']
   df3 = d['Py3']
  .
  .
  .
  df10 = d['Py22']

I then do various manipulations using pyspark. What is the best way achieving this without the redundancy? here is what i attempted..

 newname = "df"
 counter = 1
 for key in df_list.keys():
 key = newname + str(counter)
 counter+=1
 print (key)

But when i do print(df1) i get a "name 'df1' is not defined" error.

Answer 1

Yes you can use globals() provided you have all the df s globally.

newname = "df"
d = {k: globals()[newname + str(counter)] for counter, k in enumerate(d, start = 1)}

Answer 2

Let's assume you have your df in a list called dfs . I would use a combination of a comprehension and the enumerate function.

out = {newname + str(i): df for i, df in enumerate(dfs, 1)}

The function enumerate wraps an iterable and returns the tuple (index, value) . It is very convenient when you need to refer to both the value and location of each element in a list. Also note the use of tuple unpacking to give a name to both of the items returned by enumerate .