I use Pandas 'ver 0.12.0' with Python 2.7 and have a dataframe as below:
df = pd.DataFrame({'id' : [123,512,'zhub1', 12354.3, 129, 753, 295, 610],
'colour': ['black', 'white','white','white',
'black', 'black', 'white', 'white'],
'shape': ['round', 'triangular', 'triangular','triangular','square',
'triangular','round','triangular']
}, columns= ['id','colour', 'shape'])
The id Series consists of some integers and strings. Its dtype by default is object. I want to convert all contents of id to strings. I tried astype(str), which produces the output below.
df['id'].astype(str)
0 1
1 5
2 z
3 1
4 1
5 7
6 2
7 6
1) How can I convert all elements of id to String?
2) I will eventually use id for indexing for dataframes. Would having String indices in a dataframe slow things down, compared to having an integer index?
A new answer to reflect the most current practices: as of now (v1.2.4), neither astype('str') nor astype(str) work.
As per the documentation, a Series can be converted to the string datatype in the following ways:
df['id'] = df['id'].astype("string")
df['id'] = pandas.Series(df['id'], dtype="string")
df['id'] = pandas.Series(df['id'], dtype=pandas.StringDtype)
End to end example:
import pandas as pd
# Create a sample DataFrame
data = {
'Name': ['John', 'Alice', 'Bob', 'John', 'Alice'],
'Age': [25, 30, 35, 25, 30],
'City': ['New York', 'London', 'Paris', 'New York', 'London'],
'Salary': [50000, 60000, 70000, 50000, 60000],
'Category': ['A', 'B', 'C', 'A', 'B']
}
df = pd.DataFrame(data)
# Print the DataFrame
print("Original DataFrame:")
print(df)
print("\nData types:")
print(df.dtypes)
cat_cols_ = None
# Apply the code to change data types
if not cat_cols_:
# Get the columns with object data type
object_columns = df.select_dtypes(include=['object']).columns.tolist()
if len(object_columns) > 0:
print(f"\nObject columns found, converting to string: {object_columns}")
# Convert object columns to string type
df[object_columns] = df[object_columns].astype('string')
# Get the categorical columns (including string and category data types)
cat_cols_ = df.select_dtypes(include=['category', 'string']).columns.tolist()
# Print the updated DataFrame and data types
print("\nUpdated DataFrame:")
print(df)
print("\nUpdated data types:")
print(df.dtypes)
print(f"\nCategorical columns (cat_cols_): {cat_cols_}")
Original DataFrame:
Name Age City Salary Category
0 John 25 New York 50000 A
1 Alice 30 London 60000 B
2 Bob 35 Paris 70000 C
3 John 25 New York 50000 A
4 Alice 30 London 60000 B
Data types:
Name object
Age int64
City object
Salary int64
Category object
dtype: object
Object columns found, converting to string: ['Name', 'City', 'Category']
Updated DataFrame:
Name Age City Salary Category
0 John 25 New York 50000 A
1 Alice 30 London 60000 B
2 Bob 35 Paris 70000 C
3 John 25 New York 50000 A
4 Alice 30 London 60000 B
Updated data types:
Name string[python]
Age int64
City string[python]
Salary int64
Category string[python]
dtype: object
Categorical columns (cat_cols_): ['Name', 'City', 'Category']