Change column type in pandas

Step-by-step conversions

Surviving the Jungle of Numeric Conversion

If your data is like a jungle with numerical data hiding in strings, pd.to_numeric() is your machete:

df['col'] = pd.to_numeric(df['col'], errors='coerce', downcast='float') # Float like a butterfly, sting like a bee. 
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To keep your data intact, use errors='ignore':

df['col'] = pd.to_numeric(df['col'], errors='ignore') # Keep calm and ignore errors.
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The Gentle Giant of Object Conversion

df.infer_objects() is this gentle giant that can upgrade 'object' dtype to more specific types:

df = df.infer_objects() # Like finding a needle in a haystack...but easier.
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Convert dtypes: Your trusty toolbox

df.convert_dtypes() is your trusty toolbox that identifies the right tool (type) and uses it:

df = df.convert_dtypes() # Work smarter, not harder!
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Leave out automatic type inference when not necessary:

df = df.convert_dtypes(infer_objects=False) # Because sometimes, you want to drive the car yourself.
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Diving the depths of type casting

Avoid sinking your data by casting it to the right dtype. Ensuring safe casting is crucial:

if df['col'].min() < 0:
    raise ValueError("Column contains negative values, cannot use unsigned type")
df['col'] = df['col'].astype('uint32', errors='ignore') # Unsigned and proud!
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Knowing Your dtypes

Before any type conversion, a quick glance at your current dtypes using df.dtypes:

print(df.dtypes) # Like reading the ingredients before cooking.
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Then convert only the necessary columns:

df['col'] = df['col'].astype('new_type')  # like changing the flavor of your data, yum!
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There are 'hard' and 'soft' conversions, understand the difference:

# soft conversion, smooth as butter
df['col'] = df['col'].astype('uint32', errors='ignore')
# hard conversion, like your workout
df['col'] = df['col'].astype('uint32')
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Handling mixed columns

Have columns with mixed types or numeric literals? pd.to_numeric() with errors='coerce' clears the clutter:

df['col'] = df['col'].apply(pd.to_numeric, errors='coerce') # Clutter? What clutter?
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Memory optimization

To save memory, use pd.to_numeric(df['col'], downcast='integer'):

df['col'] = pd.to_numeric(df['col'], downcast='integer') # Because downcast is the new cool!
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Converting to string or categoricals can optimize memory too!

df['col'] = df['col'].astype('category')  # Like a zoo but for your data.
df['col'] = df['col'].astype(str)         # 'cos words have power!
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Pitfalls of bad casting

Avoid data corruption by not forcing the wrong dtype:

# if it's a negative, it's a no-go for casting to unsigned numbers!
if df['col'].min() < 0:
    raise ValueError("Column contains negative values, cannot use unsigned type")
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Powerful performance strategies

Prioritize columns for type casting. Handle few columns now for a performance boost!