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4 Pandas Conversion functions to know in Python!

Pandas Conversion Functions

Hello, readers! In this article, we will be focusing on 4 Important Pandas Conversion functions in Python.

So, let us get started!

The 4 Pandas Conversion Functions

In the course of this topic, we will be making use of the Bike Rental Count Prediction dataset in the examples. You can find the dataset here.


1. Pandas astype() function

The most prominently used Pandas conversion functions available in Pandas module is astype() function.

With astype() function, we can easily convert the data type of the variables from one type to another at ease. Thus, the inter-conversion between the data variables becomes easy.

Example:

In this example, initially, we check the data types of the variables of dataset using dtypes object.

BIKE.dtypes

Data type of the variables:

instant         int64
dteday         object
season          int64
yr              int64
mnth            int64
holiday         int64
weekday         int64
workingday      int64
weathersit      int64
temp          float64
atemp         float64
hum           float64
windspeed     float64
casual          int64
registered      int64
cnt             int64
dtype: object

Further to which, we now attempt to change the data type of the variable season from integer to category using astype() function.

Conversion of integer to category–

BIKE.season = BIKE.season.astype("category")
BIKE.dtypes

Output:

instant          int64
dteday          object
season        category
yr               int64
mnth             int64
holiday          int64
weekday          int64
workingday       int64
weathersit       int64
temp           float64
atemp          float64
hum            float64
windspeed      float64
casual           int64
registered       int64
cnt              int64

2. Pandas isna() function

When it comes to data analysis and pre-processing, it is very important for us to study the data and draw out meaningful information. Checking the presence of NULL values seems to be an important step prior to modeling and conversions.

With isna() function, we can easily check for the presence of NULL values, if any. It is a boolean function that returns TRUE, if it detects any NULL value within the column or dataset.

Example:

In this example, the isna() function returns false because it encounters zero missing or NULL values in the dataset.

BIKE.isna()

Output:

Conversion Functions
Conversion Functions

3. Python notna() function

With Python notna() function, we can easily segregate and look up for the values which are not equivalent to NULL or NA. That is, the notna() function returns TRUE, if it detects the presence of a non-null value.

Example:

In the below example, the notna() function returns TRUE because it does not detect any null values throughout the dataset.

BIKE.notna()

Output:

Conversion Functions Non Missing Values
Conversion Functions Non Missing Values

4. Python dataframe.copy() function

When it comes to manipulating a dataset, it is very crucial for us to have a backup of the original data as a spare copy to make changes to. This also helps us to roll back to the original situation in case the manipulations do not work out.

Example:

In this example, we have created dataframe ‘df’ that holds the copy of the dataset represented by BIKE object using copy() function.

df = BIKE.copy()
print(df)

Output:

Pandas Copy Dataframe
Pandas Copy Dataframe

Conclusion

By this, we have come to the end of this topic on Python conversion functions. Feel free to comment below, in case you come across any question.

For more such posts related to Python Programming, Stay tuned with us!

Till then, Happy Learning! 🙂