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I have a DataFrame and tried to change the dtype. I can do it like this:

#Example
ds = ds.astype({'Breite':float,'Hoehe':float,'Tiefe':float,'vol':float,'Anzahl':np.int64},axis = 1)

I was wondering if I can shorten the dict a bit to have it more readable, like this:

shorter_dict = {('Breite','Hoehe','Tiefe','vol'):float,'Anzahl':np.int64}

ds = ds.astype(shorter_dict,axis=1)

But it want take value to each element from the tuple. With my search I found a module that does it:

from multi_key_dict import multi_key_dict

k[1000, 'kilo', 'k'] = 'kilo (x1000)'

print k[1000] # will print 'kilo (x1000)' print k['k'] # will also print 'kilo (x1000)'

the same way objects can be updated, deleted: and if an object is updated using one key, the new value will be accessible using any other key, e.g. for example above: k['kilo'] = 'kilo' print k[1000] # will now print 'kilo' as value was updated

My Question now is: Is there anything directly in python that does the same?

Edit: with some help from here stackoverflow.com/a/41075523/14065969 and here https://stackoverflow.com/a/41075515/14065969

I did this and it worked:

#Example
import pandas as pd
import numpy as np

shortdict = {('Breite','Hoehe','Tiefe','vol'):float,('Anzahl',):np.int64}

df = pd.DataFrame({'Breite':10,'Hoehe':20,'Tiefe':30,'vol':100,'Anzahl':400},index = [0])

print (df)
print(df.info(),'\n'*2)

for key,value in shortdict.items():
    for inner_key in key:
        df = df.astype({inner_key : value})

print (df)
print(df.info())

OUTPUT:

   Breite  Hoehe  Tiefe  vol  Anzahl
0      10     20     30  100     400
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1 entries, 0 to 0
Data columns (total 5 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   Breite  1 non-null      int64
 1   Hoehe   1 non-null      int64
 2   Tiefe   1 non-null      int64
 3   vol     1 non-null      int64
 4   Anzahl  1 non-null      int64
dtypes: int64(5)
memory usage: 48.0 bytes
None 


   Breite  Hoehe  Tiefe    vol  Anzahl
0    10.0   20.0   30.0  100.0     400
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1 entries, 0 to 0
Data columns (total 5 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Breite  1 non-null      float64
 1   Hoehe   1 non-null      float64
 2   Tiefe   1 non-null      float64
 3   vol     1 non-null      float64
 4   Anzahl  1 non-null      int64  
dtypes: float64(4), int64(1)
memory usage: 48.0 bytes
None
[Finished in 0.7s]```
Hank Gordon
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  • there is no direct method. You will have to write function which will use `for`-loop to generate normal dictionary. – furas May 05 '21 at 17:54
  • Thanks furas I did it with a nested for loop. similar to this answer [link] https://stackoverflow.com/a/41075523/14065969 – Hank Gordon May 06 '21 at 10:44
  • so you could write your code with explanation as answer. And later you can mark it as accepted and few minutes later you can upvote your answer. You get points (reputation score) – furas May 06 '21 at 10:55
  • Thanks for that advice. But actually Im asking and writing here to get answers that helps me to learn and practice my python skills ( Im beginner). But I will write how I did it into my question. – Hank Gordon May 06 '21 at 10:59

0 Answers0