YouTipPandas Df Astype
[ Pandas Common Functions](#)
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`df.astype()` is a function in Pandas used to convert the data type of a DataFrame or Series.
In the data processing workflow, data type mismatch is a common problem. For example, numbers stored as strings, dates stored as text, etc. `astype()` allows you to explicitly convert data to the desired type, such as integer, float, string, date, etc., ensuring that the data can be correctly calculated and analyzed.
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## Basic Syntax and Parameters
`astype()` is a member function of both DataFrame and Series, called via the dot operator `.`.
### Syntax Format
DataFrame.astype(dtype, copy=True, errors='raise')Series.astype(dtype, copy=True, errors='raise')
### Parameter Description
| Parameter | Type | Required | Description | Default Value |
| --- | --- | --- | --- | --- |
| dtype | Python dtype or numpy dtype | Yes | Target data type. Can be a Python type (e.g., `int`, `str`), NumPy type (e.g., `np.int64`, `np.float32`), or Pandas type (e.g., `'int64'`, `'float64'`, `'category'`). | None |
| copy | bool | No | If `True`, always returns a new object; if `False`, modifies the original object in place when possible. | True |
| errors | str | No | Controls error handling. `'raise'` means an exception is raised on conversion failure; `'ignore'` means the original data is returned on conversion failure without raising an exception. | 'raise' |
### Return Value Description
* Returns a new DataFrame or Series, where the data types of all specified columns have been converted to the target type.
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## Examples
Let's thoroughly master the usage of `astype()` through a series of examples.
### Example 1: Converting the Data Type of a Single Column
Convert a specific column of a Series or DataFrame to a specified type.
## Instance
import pandas as pd
# Create a DataFrame with values stored as strings
data ={
'Name': ['Zhang San','Li Si','Wang Wu','Zhao Liu'],
'Age': ['25','30','35','28'],# Stored as strings
'Salary': ['5000','6000','5500','7000']# Stored as strings
}
df = pd.DataFrame(data)
print("Original data types:")
print(df.dtypes)
print("=" * 50)
# Convert "Age" column to integer type
df['Age']= df['Age'].astype(int)
print("Data types after converting Age column:")
print(df.dtypes)
print("=" * 50)
# Convert "Salary" column to float type
df['Salary']= df['Salary'].astype(float)
print("Data types after converting Salary column:")
print(df.dtypes)
print("=" * 50)
print("Converted data:")
print(df)
**Expected Output:**
Original data types:Name objectAge object # String typeSalary object # String type==================================================Data types after converting Age column:Name objectAge int64 # Integer typeSalary object==================================================Data types after converting Salary column:Name objectAge int64 Salary float64 # Float type==================================================Converted data: Name Age Salary0 Zhang San 25 5000.01 Li Si 30 6000.02 Wang Wu 35 5500.03 Zhao Liu 28 7000.0
**Code Analysis:**
1. In the original data, both age and salary are string types (`object`).
2. Use `df['column_name'].astype(int)` to convert strings to integers.
3. After conversion, numerical calculations such as sum and average can be performed.
### Example 2: Conver