Changing pandas strings to integer or float using `try: except:`-CodePudding

I have a dataframe full of strings that I would rather have as integers, or where that is not possible, floats.

I have tried to achieve this using try: except:.

The string "30" becomes an integer, but the string "40.0" becomes a float. I would like a column containing only whole-numbers as strings ("35.0", "41.0", "55.0") to become integers.

Here is a version of what I have attempted:

import pandas as pd

data = {
        'Name':['Tom', 'Dick', 'Harry'],
        'Age':['20', '21', '19'],
        'Height':['1.73', '1.80', '1.59'],
        'Score':['72.0', '69.0', '68.0']
        }
df = pd.DataFrame(data)

# Make list for looping through columns
cols = df.columns

# Check data types before manipulation
print(df.dtypes)

# Change to integer, or where not possible, float
for col in cols:
    try:
        df[col] = df[col].astype('int')
        continue
    except ValueError:
        pass
    try:
        df[col] = df[col].astype('float')
    except ValueError:
        pass

# Check if manipulation was successful
print(df.dtypes)

CodePudding user response：

As Michael already commented, you can use pd.to_numeric with errors ignore.

for col in cols:
    df[col] = pd.to_numeric(df[col], errors='ignore')

No need for try excepts then.