**pandas** is an open-source Python library that provides functionalities to manipulate and analyze data. It is used widely in machine learning to inspect data and extract useful information. This allows for the most appropriate model to be applied to the dataset. 

pandas provides an **`astype()`** function which casts a pandas object to the specified `dtype` to keep the data type consistent. 


# 
## Syntax

DataFrame.astype(dtype, copy=True, errors='raise')

  
#  
#  Arguments
1. **`dtype`**: This specifies the data type. It can also be a dictionary with data types for each of the columns.
2. **`copy`**: This can be set to `True` or `False`. If it is set to `True`, a copy will be returned instead of changes being made to the original data frame. The default is set to `True`. (Optional)
3. **`errors`**: They can be set to `raise` or `ignore`. Any errors that may come up are ignored if they are set to `ignore`. The default is set to `raise`. (Optional)

# 
## Return value
The function returns a pandas data frame object that has the changed data types.
  
##
## Example code

#import library
import pandas as pd

#initialize data
data = {
  "Time": [45, 56, 49, 64, 53],
  "Steps": [134, 153, 178, 205, 186],
  "Calories": [284.5, 234.5, 291.4, 251.0, 211.7]
}

#create a data frame
df = pd.DataFrame(data)

#print data frame
print(df)

We pass a dictionary as an argument in order to change the data type of all the columns separately. The dictionary contains the column names and the respective `dtypes` to which we want to change.

#change the data types of all the columns
new_df = df.astype({
  'Time': 'float',
  'Steps': 'float',
  'Calories': 'int64'})

#print new data frame
print(new_df)

What is astype() in pandas?

pandas' astype() function casts pandas objects to specified dtypes for data consistency.