4 Pandas Conversion Functions for Easy Data Conversion

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

So, let us begin!! 🙂

Need of Pandas Conversion functions

Python has a special place for development when it comes to Data Science and Machine Learning! It offers us various modules to deal with the data and manipulate the same.

One such module is Pandas Module.

Pandas module offers us with DataFrame as a data structure to store and manipulate the data. the beauty of it is the structure of rows and columns which makes it an essential part of data pre-processing.

While data pre-processing and manipulation, we come across the need to change the data type of the variable to a particular type for better cleaning and understanding of the data.

For this inter-conversion within the variables, we will be focusing on the below functions to perform conversion of variables:

1. Python isna() function
2. Python astype() function
3. The copy() function
4. Python notna() function

Let us begin!

1. Python isna() function

Python isna() function proves to be important in data pre-processing and cleaning of data values.

Further, with isna() function, we can easily detect for the presence of missing values. By this, the functions returns TRUE, if it detects a missing or NULL value within every variable.

Syntax:

pandas.dataframe.isna()

Example:

import pandas
info = pandas.read_csv("bike.csv")
info.isna()

Output–

2. The astype() function for conversion

With the Python astype() function, comes inter-conversion of data values. Yes, astype() function enables us to convert the data type of data from one type to another.

Thus, during the data preparation, astype() function is the key to ease.

Example–

In this example, at first, we examine the data type of the variables using the below attribute-

info.dtypes

Output– Before data-type conversion

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

Now, we convert the data type of the variable mnth from int64 to category type.

info.mnth = info.mnth.astype("category")
info.dtypes

Output — After data-type conversion

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

3. Pandas dataframe.copy() function

While we make a lot of manipulations to the data, it is definitely very essential for us to have a backup of the original data in the current working environment to reduce the overhead of extraction of data.

For the same, we have the Python copy() function. The copy() function enables us to copy the entire data and store it into a new dataset in the current environment.

Syntax:

dataframe.copy()

4. Python notna() function

On contrary to the Python isna() function, with the Python Pandas notna() function, we can easily separate the variables that do not have a NULL or missing value.

It also enables us to check against the presence of missing and returns TRUE only if the data variables do not contain a missing data value.

Syntax–

pandas.dataframe.notna()

Example–

import pandas
info = pandas.read_csv("bike.csv")
info.notna()

Output–

Conclusion

By this, we have come to the end of this topic. 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!! 🙂