Pandas DataFrame operations
Data has a variety of types. A data-type is essentially an internal construct that a programming language uses to understand how to store and operate data. The format of individual rows and columns will affect analysis performed on a dataset read into programming environment. The Pandas DataFrame is a structure that contains 2-dimensional Data and its corresponding labels.
- Types of Data
- Numeric Data Types
- Text Data Type
Numeric data types include integers and floats. Text data type is known as Objects in Pandas or Strings in Python. The names of data-types are somewhat change from those in native Python .
How to Check the Data Type in Pandas DataFrame
There are two purpose to check data types in a dataframe. Pandas automatically assigns types based on the encoding it detects from the original dataset. For a number of reasons, this assignment may be correct or incorrect. The data type for a column in a Pandas DataFrame or a Series is known as the dtype. You can use the dtype property to grab the type of a specific column
You can use the following syntax to check the data type of all columns in Pandas DataFrame :
Alternatively, you may use the syntax below to check the data type of a specific column in a DataFrame:
How to change column type in pandas?
You have four main options for converting types in pandas:
The astype() method is generally used for casting the pandas object to a specified dtype.astype() function. It can also convert any appropriate existing column to a categorical type.
Pandas to_numeric() method will try to change strings (such as non-numeric objects) into integers or floating point numbers as appropriate.
infer_objects() for converting columns of a Pandas DataFrame that have an object datatype to a more specific type.
Using infer_objects() , you can change the type of column 'a' to int64:
You can use pandas convert_dtypes() method to convert the default assigned data types to the suitable datatype automatically. There is one big advantage of using convert_dtypes()- it supports new type for missing values pd.NA along with NaN.