Change a column from dataframe

Question

I have a dataframe called df, which basically has Height and Weight about football players.

For example, the height could be represented:

1.94 1,94 1'94 or null values

How I can write a function which transform all the data to X.XX??

I would like to have it in a function, in order to replicate on other columns

I know the estructure of a function is something like that

def height(df):



    assert(isinstance(df, pd.DataFrame))
    return df

I know how to do it by commands but I'm a lost with functions in python...

Thanks in advance of all of you.

Happy holidays!!

now you can see the N,NN and N.NN there are like 4 or 5 data as N'NN

Altura
1,89 m (6 ft 2 in)
1,87 m (6 ft 2 in)
1,90 m (6 ft 3 in)
1,81 m (5 ft 11 in)
1,87 m (6 ft 2 in)[1]â€‹
1,91 m (6 ft 3 in)
1,83 m (6 ft 0 in)
1,80 m (5 ft 11 in)
1,85 m (6 ft 1 in)
1,77 m (5 ft 10 in)
1,86 m
1,87 m (6 ft 2 in)
1,75 m (5 ft 9 in)
1,87 m (6 ft 2 in)
1,79 m (5 ft 10 in)
1,78 m (5 ft 10 in)
1,82 m (6 ft 0 in)
1,91 metros
1,84 m (6 ft 0 in)
1,74 m (5 ft 9 in)
1,80 m
1,84 m (6 ft 0 in)
1,69 m (5 ft 7 in)
1,87 m (6 ft 2 in)
1,87 m (6 ft 2 in)
1,82 m (6 ft 0 in)
1,74 m (5 ft 9 in) metros
1,88 m (6 ft 2 in)

1,80 m (5 ft 11 in) metros
1,86 m (6 ft 1 in) metros
1,70 m (5 ft 7 in)
1,85 m (6 ft 1 in)
1,84 m (6 ft 0 in) metros
1,78 m (5 ft 10 in)
1,86 m (6 ft 1 in) metros
1,85 m (6 ft 1 in)
1.70 metros
1,80 m (5 ft 11 in)
1,81 metros
1,86 m (6 ft 1 in)
1,85 m (6 ft 1 in)
1,76 m (5 ft 9 in)[1]â€‹
1,71 m (5 ft 7 in)
1,77 m (5 ft 10 in)
1,69 m (5 ft 7 in)
1,95 m (6 ft 5 in)[1]â€‹
1,87 m (6 ft 2 in)
2,03 m (6 ft 8 in)
1,96 m (6 ft 5 in)
1,79 metros
1,75 metros
1,83 metros
1,93 m (6 ft 4 in)
1,85 m (6 ft 1 in)
1,78 metros
1,88 m (6 ft 2 in)
1,84 metros
1,68 m
1,70 m (5 ft 7 in)
1,70 metros
1,81 m (5 ft 11 in)
1,64 m (5 ft 5 in)
1,78 metros
1,78 metros
1,75 metros
1,83 m (6 ft 0 in)
1,81 metros
1,81 m (5 ft 11 in)
1,83 m (6 ft 0 in)
1.79 metros
1,82 metros
1,87 metros
1,90 metros
1,74 metros
1,75 m (5 ft 9 in)
1.85 m
1,75 metros
1,81 m (5 ft 11 in)
1,82 metros
1,85 m (6 ft 1 in)
1,76 m (5 ft 9 in).
1,92 m (6 ft 4 in)
1,88 m (6 ft 2 in)
1.83 metros
1,71 m (5 ft 7 in)
1.74 metros
1,80 m (5 ft 11 in)
1,78 metros

1,78 metros
1,77 m (5 ft 10 in)
1,81 metros
1,74 m (5 ft 9 in)
1,82 m (6 ft 0 in)
1,69 m (5 ft 7 in)
1,89 m (6 ft 2 in)
1,96 m (6 ft 5 in)
1,88 m
1,80 m
1,88 metros
1,84 m (6 ft 0 in)
1,84 m (6 ft 0 in)[1]â€‹ metros
1,88 m (6 ft 2 in)[1]â€‹
1,87 m (6 ft 2 in)
1,81 m (5 ft 11 in)
1,85 m (6 ft 1 in)
1,74 metros

1,67 m
1,80 metros
1,81 metros
1,62 m (5 ft 4 in)
1,80 m (5 ft 11 in)
1,80 m (5 ft 11 in).
1,84 metros
1.60metros
1,82 m (6 ft 0 in)
1,75 metros
1,81 m (5 ft 11 in)
1,86 m (6 ft 1 in)
1,85 metros
1,82 m (6 ft 0 in) metros
1,72 m (5 ft 8 in)
1,83 m (6 ft 0 in)

1,93 m (6 ft 4 in)
1,88 m (6 ft 2 in)
1,68 m (5 ft 6 in)
1,87 m (6 ft 2 in)
1'88 metros
1,83 m (6 ft 0 in)
1,84 m (6 ft 0 in)
1,85 m (6 ft 1 in)
1,82 metros
1.48 m
1,76 metros
1,82 m.
1.83 metros
1,84 m (6 ft 0 in)
1,79 m (5 ft 10 in)
1,84 metros
1,70 m (5 ft 7 in)
1,75 m (5 ft 9 in)
1,84 m (6 ft 0 in)

1,85 m (6 ft 1 in)
1,91 m (6 ft 3 in)[2]â€‹
1,82 m (6 ft 0 in)[1]â€‹
1,73 m (5 ft 8 in)
1,84 m (6 ft 0 in)
1,84 m (6 ft 0 in)
1,91 m (6 ft 3 in)
1,80 m (5 ft 11 in)
1,74 m (5 ft 9 in)[2]â€‹
1,84 m (6 ft 0 in)[1]â€‹
1,80 m (5 ft 11 in)
1,83 m (6 ft 0 in)
1,72 m (5 ft 8 in)[2]â€‹
1,85 m (6 ft 1 in)[1]â€‹
1,83 m.
1,82 m (6 ft 0 in)[2]â€‹
1,76 m (5 ft 9 in)
1,78 m (5 ft 10 in)
1,79 metros
1,85 m (6 ft 1 in)
1,85 m (6 ft 1 in)
1,85 m (6 ft 1 in)[3]â€‹
1,73 m (5 ft 8 in)
1,81 m.
1,79 m (5 ft 10 in)[1]â€‹
1,80 m (5 ft 11 in)
1,82 m (6 ft 0 in)
1,80 m (5 ft 11 in)
1,76 m.
1,77 m (5 ft 10 in)
1,87 m (6 ft 2 in)[1]â€‹
1,85 m (6 ft 1 in)
1,77 m (5 ft 10 in)
1,94 m (6 ft 4 in)[1]â€‹
1,74 m (5 ft 9 in)[4]â€‹
1,70 m (5 ft 7 in)
1,78 m (5 ft 10 in)
1,77 m (5 ft 10 in)
1,82 m (6 ft 0 in)
1,83 m (6 ft 0 in)
1,84 m (6 ft 0 in)
1,89 m (6 ft 2 in)
1,76 m (5 ft 9 in)[1]â€‹
1,72 m (5 ft 8 in)
1,71 m (5 ft 7 in)
1,74 m (5 ft 9 in)
1,78 m (5 ft 10 in)
1,88 m (6 ft 2 in)
1,75 m (5 ft 9 in)[2]â€‹
1,82 m (6 ft 0 in)[5]â€‹
1,70 m (5 ft 7 in)[5]â€‹
1,78 m (5 ft 10 in)
1,76 m (5 ft 9 in)
1,75 m (5 ft 9 in)[1]â€‹
1,10 m (3 ft 7 in)
1,82 m (6 ft 0 in)
1,71 metros
1,75 m (5 ft 9 in)
1,89 m (6 ft 2 in).[2]â€‹
1,89 metros
1,88 m (6 ft 2 in).
1,86 m (6 ft 1 in)
1,82 m (6 ft 0 in)
1,78 m (5 ft 10 in)
1,75 m (5 ft 9 in)
1,69 m (5 ft 7 in)
1,85 m (6 ft 1 in)
1,82 m (6 ft 0 in).
1,78 m (5 ft 10 in)
1,79 m (5 ft 10 in)
1,80 m (5 ft 11 in)
1,86 m (6 ft 1 in)
1,85 m (6 ft 1 in)
1,81 m (5 ft 11 in)
1,76 m (5 ft 9 in)
1,84 m (6 ft 0 in).
1,79 m (5 ft 10 in)
1,72 m (5 ft 8 in)
1,77 m (5 ft 10 in)[1]â€‹
1,89 m (6 ft 2 in)
1,80 m (5 ft 11 in)
1,96 m (6 ft 5 in)
1,90 metros
1,97 m
1,75 metros
1.76 metros
1,85 metros
1,86 m (6 ft 1 in)
1,74 metros
1.87 m
1,84 m (6 ft 0 in)
1,72 metros
1,80 m (5 ft 11 in).
1,75 metros

1,75 m (5 ft 9 in)
1,69 m (5 ft 7 in).
1,82 metros
1,75 m (5 ft 9 in)

1,78 m (5 ft 10 in)
1,70 m (5 ft 7 in)
1,72 m (5 ft 8 in)
1,88metros
1,73 m (5 ft 8 in)

1,92 metros
1.76 metros
1,78 m
1,91 metros
1,86 m (6 ft 1 in)
1,80 m (5 ft 11 in)
1,76 metros
1,83 metros
1,80 m (5 ft 11 in)[1]â€‹
1,71 m (5 ft 7 in)
1,74 m (5 ft 9 in)
1,89 m (6 ft 2 in)
1,81 m (5 ft 11 in)
1,86 m (6 ft 1 in)
1,96 m
1,70 m (5 ft 7 in)[2]â€‹
1,78 metros
1,82[1]â€‹ metros
1,86 m (6 ft 1 in)
1,86 m (6 ft 1 in)
1,70 m (5 ft 7 in)
1,79 m (5 ft 10 in)
1,75 metros
1,91 m (6 ft 3 in).
1,95 m (6 ft 5 in)
1,87 m (6 ft 2 in)
1,92 metros
1,80[1]â€‹ metros
1,83 metros
1.96 metros
1,92 metros
1,79 m (5 ft 10 in)
1,73 m (5 ft 8 in) metros
1,73 m (5 ft 8 in)
1,75 metros
1.77 metros
1,70 metros
1,80 metros
1,79 metros
1,78 m (5 ft 10 in)
1,85 m
1,71 m (5 ft 7 in)
1,80 m (5 ft 11 in)
1,73 metros
1,75 m (5 ft 9 in)
1,86 metros
1,90 m (6 ft 3 in)
1.74 metros
1,86 m (6 ft 1 in)
1,85 metros
1'94 metros
1,80 metros
1,88 metros
1,77 m (5 ft 10 in)
1,87 m (6 ft 2 in)
1,82 metros
1.77 metros
1,88 m (6 ft 2 in)
1,75 metros
1,70 m (5 ft 7 in)[1]â€‹
1,80 m (5 ft 11 in)
1,82 m (6 ft 0 in)
1,91 m (6 ft 3 in)
1,69 metros
1,70 metros
1,80 m (5 ft 11 in)
1,78 m (5 ft 10 in).
1,84 m (6 ft 0 in)
1,81 m (5 ft 11 in)
1,90[2]â€‹ metros
1.86 metros
1,77 metros

1,90 m (6 ft 3 in)
1.91 metros
1,75 m (5 ft 9 in)
1,80 m (5 ft 11 in)
1,82 metros
1.89 metros
1,84 metros
1,85 m (6 ft 1 in)
1,74 m (5 ft 9 in)
1.76metros
1,78 metros
1,80 m (5 ft 11 in)
1,85 m (6 ft 1 in)
1,80 m (5 ft 11 in)
1.72 metros
1,80 m (5 ft 11 in)
1,83 m (6 ft 0 in)
1,78 m (5 ft 10 in)
1,79 metros
1,80 m (5 ft 11 in)
1,79 m (5 ft 10 in)
1,74 m (5 ft 9 in)
1,78 metros
1,9
1,75 m
1,87 metros
1,85[1]â€‹ metros
1,79 m (5 ft 10 in)
1,88 m (6 ft 2 in)
1,82 metros
1.76 m
1,76 m (5 ft 9 in)
1.80 metros
1,88 metros
1,77 metros
1,78 metros
1,85 m (6 ft 1 in) metros
1,87 m
1,10 m (3 ft 7 in)
1,94 m (6 ft 4 in)
1,70 metros
1,84 metros
1'84 metros
1,85 metros
1,8
1,70 metros
1,68 metros
1,86 m
1,79 m (5 ft 10 in)
1,72 metros
1,78 m (5 ft 10 in)
1,85 m (6 ft 1 in)
1.82 m
1,69 m (5 ft 7 in)
1,78 m (5 ft 10 in)
1,95 metros
1,90 m (6 ft 3 in)
1,93 m.
1,93 m (6 ft 4 in)
1,86 m (6 ft 1 in)
1,83 metros
1.78 metros
1,74 metros
1,79 m (5 ft 10 in)
1,74 m (5 ft 9 in)
1,84 m
1,84 metros
1,78 m (5 ft 10 in)
1,86 m (6 ft 1 in)
1,84 metros
1,71 m (5 ft 7 in)
1,78 m (5 ft 10 in)
1,83 metros
1,72 m (5 ft 8 in)
1,72 metros
1,79 metros
1,78 m
1,91 metros
1,88 m (6 ft 2 in)[1]â€‹
1,77 m (5 ft 10 in)
1,88 m (6 ft 2 in)
1'88 metros
1,93 m (6 ft 4 in)
1.84 metros
1,84 m (6 ft 0 in)
1,80 m (5 ft 11 in)
1,72 metros
1,73 m (5 ft 8 in)
1,76 m (5 ft 9 in)
1,89 m (6 ft 2 in)
1,82 m (6 ft 0 in)
1,80 m (5 ft 11 in)
1,89 m (6 ft 2 in)
1,80 m (5 ft 11 in)
1,78 m (5 ft 10 in)
1,80 m (5 ft 11 in)[1]â€‹
1,88 m (6 ft 2 in)
1,80 m (5 ft 11 in)
1,65 m (5 ft 5 in)
1,84 m (6 ft 0 in)
1,75 m (5 ft 9 in)
1,87 metros
1,78 m (5 ft 10 in)
1'76 metros
1.82 metros
1,92 m (6 ft 4 in)[1]â€‹
1,73 metros
1,92 m (6 ft 4 in)
1,85 metros
1,68 metros
1,82 metros
1,82 metros
1,84 metros
1,84 metros
1,87 metros
1,89 metros
1,83 m (6 ft 0 in)
1,72 m (5 ft 8 in)
1,77 m (5 ft 10 in)

1,69 m (5 ft 7 in)
1,80 m (5 ft 11 in)

1,76 metros
1,81 metros
1,78 m (5 ft 10 in)
1,78 m (5 ft 10 in)
1,74 m (5 ft 9 in)
1,89 m (6 ft 2 in)
1,90 m (6 ft 3 in)
1,88 m (6 ft 2 in)
1,82 m (6 ft 0 in)
1,83 metros
1,93 metros
1,86 m (6 ft 1 in)
1,85 m (6 ft 1 in)
1,71 m (5 ft 7 in)
1,76 m (5 ft 9 in)
1,83 m (6 ft 0 in)
1,84 m (6 ft 0 in)
1,78 m (5 ft 10 in)
1,80 m (5 ft 11 in)
1,78 m (5 ft 10 in)
1,93 m (6 ft 4 in)
1,82 metros
1,84 m (6 ft 0 in)
1,83 m (6 ft 0 in)
1,84 m
1,67 m (5 ft 6 in)
1.81
1,80 m (5 ft 11 in)
1,75 metros
1,85 m (6 ft 1 in)

@rogarui not quite what you want but it's a good check to see if it works on the entirety of your data - does: df['Altura'].str.extract("^(?P<m>\d)[,.'](?P<cm>\d+)").astype(int) give you a dataframe ? — Jon Clements
– Jon Clements, Commented Dec 29, 2019 at 13:36
no sorry, if I copy this line and erro show in the console: ValueError: cannot convert float NaN to integer — rogarui
– rogarui, Commented Dec 29, 2019 at 13:44
Well - it works on the data you posted so... again... we're missing important information here — Jon Clements
– Jon Clements, Commented Dec 29, 2019 at 13:47

milos.ai · Accepted Answer · 2019-12-29 13:02:35Z

1

Maybe something like this can help. Idea is to get first 4 characters , replace comma with dot and cast to float

df  = df.assign(height = df.Altura.str.slice(0,4).str.replace(',','.').astype(float))

As a function it can look like this:

def height(df, column_name):

    df  = df.assign(height = df[column_name].str.slice(0,4).str.replace(',','.').astype(float))

    return df

And you can create height by invoking command:

height(df,'Altura')

edited Dec 29, 2019 at 13:02

answered Dec 29, 2019 at 12:58

milos.ai

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Comments

Gambitier · Accepted Answer · 2019-12-29 13:54:32Z

0

maybe you'd like to do this way:

import numpy as np
def convert_height(height_str) 
    if(height_str.isspace()):
            return np.nan
    else:
            height = height_str[:3].strip()
            if ',' in height:
                    height = height.replace(',','.')


            if "'" in height:
                    height = height.replace("'",".")


            if '`' in height:
                    height = height.replace('`','.')

    return float(height)

dataset['height_col'] = dataset['height_col'].apply(convert_height)

edited Dec 29, 2019 at 13:54

answered Dec 29, 2019 at 13:11

Gambitier

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