在凌乱的 pandas 数据帧中转换 dtypes？

Question

I have a big data-frame. I want to convert them to the appropriate dtype. The problem is that in several numeric columns there are strings. I know about convert_dtypes and to_numeric. With the former the problems is that it doesn't infer a column as int/float as soon as there strings there, to_numeric on the other hand has "coerce" which turns all the invalid examples to nan. The problem with to_numeric is that there are several columns that are strings, so I can't just run it on all columns.

So I am looking for a function that convert dtypes to numeric if there is a certain % of numeric values in it. It would be great if one could set the threshold for this.

As mentioned before the dataset is large, so I would prefer some solution that handles all the columns automatically.

Answer 1

Use custom function with convert columns to numeric and if match condition return numeric column else original column in DataFrame.apply :

print (df)
   a  b  c  d  e
0  1  5  4  3  8
1  7  8  9  f  9
2  c  c  g  g  4
3  4  t  r  e  4

def f(x, thresh):
    y = pd.to_numeric(x, errors='coerce')
    return y if y.notna().mean() > thresh else x

thresh = 0.7
df1 = df.apply(f, args= (thresh,))
print (df1)
     a  b  c  d  e
0  1.0  5  4  3  8
1  7.0  8  9  f  9
2  NaN  c  g  g  4
3  4.0  t  r  e  4

print (df1.dtypes)
a    float64
b     object
c     object
d     object
e      int64
dtype: object

Modified solution with missing values (if exist):

print (df)
   a  b    c  d  e
0  1  5    4  3  8
1  7  8  NaN  f  9
2  c  c  NaN  g  4
3  4  t    r  e  4

def f(x, thresh):
    y = pd.to_numeric(x, errors='coerce')
    return y if (y.notna() | x.isna()).mean() > thresh else x

thresh = 0.7
df1 = df.apply(f, args= (thresh,))
print (df1)
     a  b    c  d  e
0  1.0  5  4.0  3  8
1  7.0  8  NaN  f  9
2  NaN  c  NaN  g  4
3  4.0  t  NaN  e  4

print (df1.dtypes)
a    float64
b     object
c    float64
d     object
e      int64
dtype: object