What is astype() function in Python

04010

Understanding astype() in Python

The astype() function is a powerful method in Python, primarily used in the pandas library for converting a column or a dataset in a DataFrame or Series to a specific data type. It is also available in NumPy for casting array elements to a different type.

Basic Usage of astype()

The astype() function is used to cast the data type of a pandas object (like a Series or DataFrame) or a NumPy array into another type.

Syntax for pandas: 

<span>DataFrame</span><span>.</span><span>astype</span><span>(</span><span>dtype</span><span>,</span> <span>copy</span><span>=</span><span>True</span><span>,</span> <span>errors</span><span>=</span><span>'</span><span>raise</span><span>'</span><span>)</span>
<span>DataFrame</span><span>.</span><span>astype</span><span>(</span><span>dtype</span><span>,</span> <span>copy</span><span>=</span><span>True</span><span>,</span> <span>errors</span><span>=</span><span>'</span><span>raise</span><span>'</span><span>)</span>
DataFrame.astype(dtype, copy=True, errors='raise')

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Syntax for NumPy: 

<span>ndarray</span><span>.</span><span>astype</span><span>(</span><span>dtype</span><span>,</span> <span>order</span><span>=</span><span>'</span><span>K</span><span>'</span><span>,</span> <span>casting</span><span>=</span><span>'</span><span>unsafe</span><span>'</span><span>,</span> <span>subok</span><span>=</span><span>True</span><span>,</span> <span>copy</span><span>=</span><span>True</span><span>)</span>
<span>ndarray</span><span>.</span><span>astype</span><span>(</span><span>dtype</span><span>,</span> <span>order</span><span>=</span><span>'</span><span>K</span><span>'</span><span>,</span> <span>casting</span><span>=</span><span>'</span><span>unsafe</span><span>'</span><span>,</span> <span>subok</span><span>=</span><span>True</span><span>,</span> <span>copy</span><span>=</span><span>True</span><span>)</span>
ndarray.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

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Key Parameters

1. dtype

The target data type to which you want to convert the data. This can be specified using:

  • A single type (e.g., float, int, str).
  • A dictionary mapping column names to types (for pandas DataFrames).

2. copy (pandas and NumPy)

  • Default: True
  • Purpose: Whether to return a copy of the original data (if True) or modify it in place (if False).

3. errors (pandas only)

  • Options:
    • 'raise' (default): Raise an error if conversion fails.
    • 'ignore': Silently ignore errors.

4. order (NumPy only)

  • Controls the memory layout of the output array. Options:
    • 'C': C-contiguous order.
    • 'F': Fortran-contiguous order.
    • 'A': Use Fortran order if input is Fortran-contiguous, otherwise C order.
    • 'K': Match the layout of the input array.

5. casting (NumPy only)

  • Controls casting behavior:
    • 'no': No casting allowed.
    • 'equiv': Only byte-order changes allowed.
    • 'safe': Only casts that preserve values are allowed.
    • 'same_kind': Only safe casts or casts within a kind (e.g., float -> int) are allowed.
    • 'unsafe': Any data conversion is allowed.

6. subok (NumPy only)

  • If True, sub-classes are passed through; if False, the returned array will be a base-class array.

Examples

1. Basic Conversion in pandas 

<span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span># Example DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>1</span><span>'</span><span>,</span> <span>'</span><span>2</span><span>'</span><span>,</span> <span>'</span><span>3</span><span>'</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>1.5</span><span>,</span> <span>2.5</span><span>,</span> <span>3.5</span><span>]})</span>
<span># Convert column 'A' to integer </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>int</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>dtypes</span><span>)</span>
<span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>

<span># Example DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>1</span><span>'</span><span>,</span> <span>'</span><span>2</span><span>'</span><span>,</span> <span>'</span><span>3</span><span>'</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>1.5</span><span>,</span> <span>2.5</span><span>,</span> <span>3.5</span><span>]})</span>

<span># Convert column 'A' to integer </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>int</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>dtypes</span><span>)</span>
import pandas as pd

# Example DataFrame df = pd.DataFrame({'A': ['1', '2', '3'], 'B': [1.5, 2.5, 3.5]})

# Convert column 'A' to integer df['A'] = df['A'].astype(int)
print(df.dtypes)

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Output: 

A     int64
B    float64
dtype: object
A     int64
B    float64
dtype: object
A     int64
B    float64
dtype: object

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2. Dictionary Mapping for Multiple Columns 

<span># Convert multiple columns </span><span>df</span> <span>=</span> <span>df</span><span>.</span><span>astype</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>float</span><span>,</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>int</span><span>})</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>dtypes</span><span>)</span>
<span># Convert multiple columns </span><span>df</span> <span>=</span> <span>df</span><span>.</span><span>astype</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>float</span><span>,</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>int</span><span>})</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>dtypes</span><span>)</span>
# Convert multiple columns df = df.astype({'A': float, 'B': int})
print(df.dtypes)

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Output: 

A    float64
B      int64
dtype: object
A    float64
B      int64
dtype: object
A    float64
B      int64
dtype: object

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3. Using errors='ignore' 

<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>1</span><span>'</span><span>,</span> <span>'</span><span>two</span><span>'</span><span>,</span> <span>'</span><span>3</span><span>'</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>1.5</span><span>,</span> <span>2.5</span><span>,</span> <span>3.5</span><span>]})</span>
<span># Attempt conversion with errors='ignore' </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>int</span><span>,</span> <span>errors</span><span>=</span><span>'</span><span>ignore</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>1</span><span>'</span><span>,</span> <span>'</span><span>two</span><span>'</span><span>,</span> <span>'</span><span>3</span><span>'</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>1.5</span><span>,</span> <span>2.5</span><span>,</span> <span>3.5</span><span>]})</span>

<span># Attempt conversion with errors='ignore' </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>int</span><span>,</span> <span>errors</span><span>=</span><span>'</span><span>ignore</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
df = pd.DataFrame({'A': ['1', 'two', '3'], 'B': [1.5, 2.5, 3.5]})

# Attempt conversion with errors='ignore' df['A'] = df['A'].astype(int, errors='ignore')
print(df)

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Output: 

      A    B
0     1  1.5
1   two  2.5
2     3  3.5
      A    B
0     1  1.5
1   two  2.5
2     3  3.5
      A    B
0     1  1.5
1   two  2.5
2     3  3.5

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  • Conversion fails for 'two', but no error is raised.

4. Using astype() in NumPy 

<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>
<span># Example array </span><span>arr</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>1.1</span><span>,</span> <span>2.2</span><span>,</span> <span>3.3</span><span>])</span>
<span># Convert to integer </span><span>arr_int</span> <span>=</span> <span>arr</span><span>.</span><span>astype</span><span>(</span><span>int</span><span>)</span>
<span>print</span><span>(</span><span>arr_int</span><span>)</span>
<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>

<span># Example array </span><span>arr</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>1.1</span><span>,</span> <span>2.2</span><span>,</span> <span>3.3</span><span>])</span>

<span># Convert to integer </span><span>arr_int</span> <span>=</span> <span>arr</span><span>.</span><span>astype</span><span>(</span><span>int</span><span>)</span>
<span>print</span><span>(</span><span>arr_int</span><span>)</span>
import numpy as np

# Example array arr = np.array([1.1, 2.2, 3.3])

# Convert to integer arr_int = arr.astype(int)
print(arr_int)

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Output: 

[1 2 3]
[1 2 3]
[1 2 3]

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5. Casting in NumPy with casting='safe' 

<span>arr</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>1.1</span><span>,</span> <span>2.2</span><span>,</span> <span>3.3</span><span>])</span>
<span># Attempt an unsafe conversion </span><span>try</span><span>:</span>
    <span>arr_str</span> <span>=</span> <span>arr</span><span>.</span><span>astype</span><span>(</span><span>str</span><span>,</span> <span>casting</span><span>=</span><span>'</span><span>safe</span><span>'</span><span>)</span>
<span>except</span> <span>TypeError</span> <span>as</span> <span>e</span><span>:</span>
    <span>print</span><span>(</span><span>e</span><span>)</span>
<span>arr</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([</span><span>1.1</span><span>,</span> <span>2.2</span><span>,</span> <span>3.3</span><span>])</span>

<span># Attempt an unsafe conversion </span><span>try</span><span>:</span>
    <span>arr_str</span> <span>=</span> <span>arr</span><span>.</span><span>astype</span><span>(</span><span>str</span><span>,</span> <span>casting</span><span>=</span><span>'</span><span>safe</span><span>'</span><span>)</span>
<span>except</span> <span>TypeError</span> <span>as</span> <span>e</span><span>:</span>
    <span>print</span><span>(</span><span>e</span><span>)</span>
arr = np.array([1.1, 2.2, 3.3])

# Attempt an unsafe conversion try:
    arr_str = arr.astype(str, casting='safe')
except TypeError as e:
    print(e)

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Output: 

Cannot cast array data from dtype('float64') to dtype('<U32') according to the rule 'safe'
Cannot cast array data from dtype('float64') to dtype('<U32') according to the rule 'safe'
Cannot cast array data from dtype('float64') to dtype('<U32') according to the rule 'safe'

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6. Handling Non-Numeric Types in pandas 

<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>2022-01-01</span><span>'</span><span>,</span> <span>'</span><span>2023-01-01</span><span>'</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>True</span><span>'</span><span>,</span> <span>'</span><span>False</span><span>'</span><span>]})</span>
<span># Convert to datetime and boolean </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>pd</span><span>.</span><span>to_datetime</span><span>(</span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>])</span>
<span>df</span><span>[</span><span>'</span><span>B</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>B</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>bool</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>dtypes</span><span>)</span>
<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>2022-01-01</span><span>'</span><span>,</span> <span>'</span><span>2023-01-01</span><span>'</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>True</span><span>'</span><span>,</span> <span>'</span><span>False</span><span>'</span><span>]})</span>

<span># Convert to datetime and boolean </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>pd</span><span>.</span><span>to_datetime</span><span>(</span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>])</span>
<span>df</span><span>[</span><span>'</span><span>B</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>B</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>bool</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>dtypes</span><span>)</span>
df = pd.DataFrame({'A': ['2022-01-01', '2023-01-01'], 'B': ['True', 'False']})

# Convert to datetime and boolean df['A'] = pd.to_datetime(df['A'])
df['B'] = df['B'].astype(bool)
print(df.dtypes)

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Output: 

A    datetime64[ns]
B             bool
dtype: object
A    datetime64[ns]
B             bool
dtype: object
A    datetime64[ns]
B             bool
dtype: object

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7. Memory Optimization Using astype()

Code: 

<span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span># Original DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>1.1</span><span>,</span> <span>2.2</span><span>,</span> <span>3.3</span><span>]})</span>
<span>print</span><span>(</span><span>"</span><span>Original memory usage:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>memory_usage</span><span>())</span>
<span># Downcast numerical types </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>'</span><span>int8</span><span>'</span><span>)</span>
<span>df</span><span>[</span><span>'</span><span>B</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>B</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>'</span><span>float32</span><span>'</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Optimized memory usage:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>memory_usage</span><span>())</span>
<span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>

<span># Original DataFrame </span><span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>1</span><span>,</span> <span>2</span><span>,</span> <span>3</span><span>],</span> <span>'</span><span>B</span><span>'</span><span>:</span> <span>[</span><span>1.1</span><span>,</span> <span>2.2</span><span>,</span> <span>3.3</span><span>]})</span>
<span>print</span><span>(</span><span>"</span><span>Original memory usage:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>memory_usage</span><span>())</span>

<span># Downcast numerical types </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>'</span><span>int8</span><span>'</span><span>)</span>
<span>df</span><span>[</span><span>'</span><span>B</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>B</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>'</span><span>float32</span><span>'</span><span>)</span>

<span>print</span><span>(</span><span>"</span><span>Optimized memory usage:</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>.</span><span>memory_usage</span><span>())</span>
import pandas as pd

# Original DataFrame df = pd.DataFrame({'A': [1, 2, 3], 'B': [1.1, 2.2, 3.3]})
print("Original memory usage:")
print(df.memory_usage())

# Downcast numerical types df['A'] = df['A'].astype('int8')
df['B'] = df['B'].astype('float32')

print("Optimized memory usage:")
print(df.memory_usage())

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Output:

Before Optimization (Original Memory Usage): 

Index    128
A         24
B         24
dtype: int64
Index    128
A         24
B         24
dtype: int64
Index    128
A         24
B         24
dtype: int64

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After Optimization (Optimized Memory Usage): 

Index    128
A          3
B         12
dtype: int64
Index    128
A          3
B         12
dtype: int64
Index    128
A          3
B         12
dtype: int64

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Explanation:

  • Original Memory Usage:

    • Column A as int64 uses 24 bytes (8 bytes per element × 3 elements).
    • Column B as float64 uses 24 bytes (8 bytes per element × 3 elements).

  • Optimized Memory Usage:

    • Column A as int8 uses 3 bytes (1 byte per element × 3 elements).
    • Column B as float32 uses 12 bytes (4 bytes per element × 3 elements).

The memory usage is significantly reduced by using smaller data types, especially when working with large datasets.

Common Pitfalls

  1. Invalid Conversion: Converting incompatible types (e.g., strings to numeric types when non-numeric values exist). 
   <span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>1</span><span>'</span><span>,</span> <span>'</span><span>two</span><span>'</span><span>,</span> <span>'</span><span>3</span><span>'</span><span>]})</span>
   <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>int</span><span>)</span>  <span># This will raise a ValueError </span>
   <span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>1</span><span>'</span><span>,</span> <span>'</span><span>two</span><span>'</span><span>,</span> <span>'</span><span>3</span><span>'</span><span>]})</span>
   <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>int</span><span>)</span>  <span># This will raise a ValueError </span>
   df = pd.DataFrame({'A': ['1', 'two', '3']})
   df['A'] = df['A'].astype(int)  # This will raise a ValueError

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  1. Silent Errors with errors='ignore': Use with caution as it may silently fail to convert.

  2. Loss of Precision: Converting from a higher-precision type (e.g., float64) to a lower-precision type (e.g., float32).

Advanced Examples

1. Complex Data Type Casting 

<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>1.1</span><span>'</span><span>,</span> <span>'</span><span>2.2</span><span>'</span><span>,</span> <span>'</span><span>3.3</span><span>'</span><span>]})</span>
<span># Cast to float and then to int </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>float</span><span>).</span><span>astype</span><span>(</span><span>int</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>({</span><span>'</span><span>A</span><span>'</span><span>:</span> <span>[</span><span>'</span><span>1.1</span><span>'</span><span>,</span> <span>'</span><span>2.2</span><span>'</span><span>,</span> <span>'</span><span>3.3</span><span>'</span><span>]})</span>

<span># Cast to float and then to int </span><span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'</span><span>A</span><span>'</span><span>].</span><span>astype</span><span>(</span><span>float</span><span>).</span><span>astype</span><span>(</span><span>int</span><span>)</span>
<span>print</span><span>(</span><span>df</span><span>)</span>
df = pd.DataFrame({'A': ['1.1', '2.2', '3.3']})

# Cast to float and then to int df['A'] = df['A'].astype(float).astype(int)
print(df)

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Output: 

   A
0  1
1  2
2  3
   A
0  1
1  2
2  3
   A
0  1
1  2
2  3

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2. Using astype() in NumPy for Structured Arrays 

<span># Structured array </span><span>data</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([(</span><span>1</span><span>,</span> <span>2.5</span><span>),</span> <span>(</span><span>2</span><span>,</span> <span>3.5</span><span>)],</span> <span>dtype</span><span>=</span><span>[(</span><span>'</span><span>x</span><span>'</span><span>,</span> <span>'</span><span>i4</span><span>'</span><span>),</span> <span>(</span><span>'</span><span>y</span><span>'</span><span>,</span> <span>'</span><span>f4</span><span>'</span><span>)])</span>
<span># Convert data type </span><span>data</span> <span>=</span> <span>data</span><span>.</span><span>astype</span><span>([(</span><span>'</span><span>x</span><span>'</span><span>,</span> <span>'</span><span>f8</span><span>'</span><span>),</span> <span>(</span><span>'</span><span>y</span><span>'</span><span>,</span> <span>'</span><span>i8</span><span>'</span><span>)])</span>
<span>print</span><span>(</span><span>data</span><span>)</span>
<span># Structured array </span><span>data</span> <span>=</span> <span>np</span><span>.</span><span>array</span><span>([(</span><span>1</span><span>,</span> <span>2.5</span><span>),</span> <span>(</span><span>2</span><span>,</span> <span>3.5</span><span>)],</span> <span>dtype</span><span>=</span><span>[(</span><span>'</span><span>x</span><span>'</span><span>,</span> <span>'</span><span>i4</span><span>'</span><span>),</span> <span>(</span><span>'</span><span>y</span><span>'</span><span>,</span> <span>'</span><span>f4</span><span>'</span><span>)])</span>

<span># Convert data type </span><span>data</span> <span>=</span> <span>data</span><span>.</span><span>astype</span><span>([(</span><span>'</span><span>x</span><span>'</span><span>,</span> <span>'</span><span>f8</span><span>'</span><span>),</span> <span>(</span><span>'</span><span>y</span><span>'</span><span>,</span> <span>'</span><span>i8</span><span>'</span><span>)])</span>
<span>print</span><span>(</span><span>data</span><span>)</span>
# Structured array data = np.array([(1, 2.5), (2, 3.5)], dtype=[('x', 'i4'), ('y', 'f4')])

# Convert data type data = data.astype([('x', 'f8'), ('y', 'i8')])
print(data)

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Output: 

[(1., 2) (2., 3)]
[(1., 2) (2., 3)]
[(1., 2) (2., 3)]

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Summary

The astype() function is a versatile tool for data type conversion in both pandas and NumPy. It allows fine-grained control over casting behavior, memory optimization, and error handling. Proper use of its parameters, such as errors in pandas and casting in NumPy, ensures robust and efficient data type transformations.

原文链接：What is astype() function in Python

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