字典到全局环境python

Question

I have attempted to open a list of cvs files in a loop using python. I started with the idea to open the files to a dictionary as I been told to do not attempt to dynamically create names on the fly, I tried the following code

Load all csv files

 filenames = ["broaderRelationsSkillPillar.csv","ISCOGroups_en.csv"]

 dataframes ={}    ## create a dictionary 

Create the list of three DataFrames: dataframes

for i in filenames :
    dataframes[i] = pd.read_csv(i) 

until here it all happens smoothly.

for k ,v in dataframes.items():
    [k] = pd.DataFrame.from_dict(dataframes[k])

note - my issue happens here ,my result here is only one of the 2 data frames

would I be able to open this csv files directly froma loop and name them on the fly? I have around 20 csv's and I'm trying to automate a bit the code. tks

Question edited to facilitate support

create dictionaies

d = {'col1': [1, 2], 'col2': [3, 4]} 
a = {'col3': [1, 2], 'col4': [3, 4]} 
c = {'col3': [1, 2], 'col4': [3, 4]} 

Pass to a data frame

d= pd.DataFrame(data=d)
a= pd.DataFrame(data=a)
c= pd.DataFrame(data=c)

create a list of dataframes

filenames = [a ,d ,c]

create a dictionary of dataframes

dataframes ={}    ## create a dictionary 

for i in filenames :
    dataframes[i] = i

del a , c, d    

from a dictionary of data frames back to data frames ( here is where i failed , why?)

for k ,v in dataframes.items():
    k = pd.from_dict(dataframes[k])

Answer 1

I believe need dict comprehension for dictionary of DataFrames with keys by filenames:

dataframes = {i:pd.read_csv(i) for i in filenames}
print (dataframes['broaderRelationsSkillPillar.csv'])
print (dataframes['ISCOGroups_en.csv'])

Or is possible removing last .csv by indexing:

dataframes = {i[:-4]: pd.read_csv(i) for i in filenames}
print (dataframes['broaderRelationsSkillPillar'])
print (dataframes['ISCOGroups_en'])

---

Sample DataFrames:

df1 = pd.DataFrame({'A': ['a','a'],'B': list(range(2))})
df2 = pd.DataFrame({'C': ['b','f','s'],'D': list(range(3))})
df3 = pd.DataFrame({'E': ['f','g','h'],'F': list(range(3))})
print (df1)
   A  B
0  a  0
1  a  1

print (df2)
   C  D
0  b  0
1  f  1
2  s  2

print (df3)
   E  F
0  f  0
1  g  1
2  h  2

Created dictionary of DataFrames :

dataframes = {'file1':df1, 'file2':df2, 'file3':df3}
print (dataframes)
{'file1':    A  B
0  a  0
1  a  1, 'file2':    C  D
0  b  0
1  f  1
2  s  2, 'file3':    E  F
0  f  0
1  g  1
2  h  2}

For DataFrame select by key - ere by file1 :

print (dataframes['file1'])
   A  B
0  a  0
1  a  1

In loop v is DataFrame :

for k ,v in dataframes.items():
    print (k)
    print (v)
    print (type(v))
file1
   A  B
0  a  0
1  a  1
<class 'pandas.core.frame.DataFrame'>
file2
   C  D
0  b  0
1  f  1
2  s  2
<class 'pandas.core.frame.DataFrame'>
file3
   E  F
0  f  0
1  g  1
2  h  2
<class 'pandas.core.frame.DataFrame'>

If want modify DataFrames in loop, you need to reference the original df by using key of dictionary :

for k ,v in dataframes.items():
    #modify df - e.g. add `a` to first column
    v.iloc[:, 0] = v.iloc[:, 0] + 'a'
    print (v)
    dataframes[k] = v
    A  B
0  aa  0
1  aa  1
    C  D
0  ba  0
1  fa  1
2  sa  2
    E  F
0  fa  0
1  ga  1
2  ha  2

Dictionary of DataFrames :

print (dataframes)
{'file1':     A  B
0  aa  0
1  aa  1, 'file2':     C  D
0  ba  0
1  fa  1
2  sa  2, 'file3':     E  F
0  fa  0
1  ga  1
2  ha  2}

Check one DataFrame :

print (dataframes['file1'])
    A  B
0  aa  0
1  aa  1