Data analysis and pre-processing tasks involve converting variables to different data types, ensuring datasets are free from NULL values and creating backup copies for future reference. In Python, Pandas is a reliable library providing flexible data structures and analysis tools for effective data management.
In this article, we will explore Pandas Conversion Functions. We will dive into four subtopics with practical applications of each to help readers attain a better understanding.
Let’s get started!
1.to Pandas astype() function
The astype() function is a primary Pandas conversion function for changing the data type of a variable. Often, datasets are stored as strings, and analysts need to convert them to numeric data types such as integers, float, or category data types.
Here, astype() function comes in handy. Let’s dive into an example demonstrating the functionality of astype():
– Example: Changing data type of variable to integer
“`
import pandas as pd
data = {‘Name’: [‘Ali’, ‘Ahmed’, ‘Neha’, ‘Sara’], ‘Age’: [’21’, ’22’, ’23’, ’21’], ‘Gender’: [‘M’, ‘M’, ‘F’, ‘F’]}
df = pd.DataFrame(data)
print(df)
print(df.dtypes)
df[‘Age’] = df[‘Age’].astype(int)
print(df)
print(df.dtypes)
“`
In this example, we convert the ‘Age’ column’s data type in the dataframe from String to Int via astype() function.
The original dataset looks like this:
| | Name | Age | Gender |
|—|———|—–|——–|
| 0 | Ali | 21 | M |
| 1 | Ahmed | 22 | M |
| 2 | Neha | 23 | F |
| 3 | Sara | 21 | F |
After conversion, the data type conversion is shown as follows:
| | Name | Age | Gender |
|—|———|—–|——–|
| 0 | Ali | 21 | M |
| 1 | Ahmed | 22 | M |
| 2 | Neha | 23 | F |
| 3 | Sara | 21 | F |
astype() function can also be used to convert a column to a category data type, Boolean value, or any valid Python object. 2.
Checking for NULL values using isna() function
Handling missing or NULL values is a crucial pre-processing task in data analysis. In Python, isna() function is a Pandas conversion function that checks for NULL values in a dataset.
The function returns a boolean value of either true or false based on whether a column contains a null value or not. Let’s see an example:
– Example: Checking for null values
“`
import pandas as pd
import numpy as np
data = {‘Name’: [‘Ali’, ‘Ahmed’, np.nan, ‘Sara’], ‘Age’: [’21’, ’22’, ’23’, ‘NaN’], ‘Gender’:[‘M’, np.nan, ‘F’, ‘F’]}
df = pd.DataFrame(data)
print(df.isna())
“`
In this example, we use the isna() function to check for NULL values in the ‘df’ dataset.
After execution, this function prints the number of NULL values for each column in the dataset. Its output will be as follows:
| | Name | Age | Gender |
|—|——–|——-|——–|
| 0 | False | False | False |
| 1 | False | False | True |
| 2 | True | False | False |
| 3 | False | True | False |
3.
Segregating non-null values using notna() function
Sometimes, analysts may need to segregate non-NULL values from a dataset. In Python, notna() function is a Pandas conversion function that returns a boolean array reflecting the locations of non-missing values.
Here’s an example demonstrating the function:
– Example: Segregating non-null values
“`
import pandas as pd
import numpy as np
data = {‘Name’: [‘Ali’, ‘Ahmed’, np.nan, ‘Sara’], ‘Age’: [’21’, ’22’, ’23’, ‘NaN’], ‘Gender’:[‘M’, np.nan, ‘F’, ‘F’]}
df = pd.DataFrame(data)
print(df.notna())
“`
In this example, we use the notna() function to segregate non-null values in the ‘df’ dataset. After execution, this function prints the non-null values for each column in the dataset.
Its output will be as follows:
| | Name | Age | Gender |
|—|——–|——-|——–|
| 0 | True | True | True |
| 1 | True | True | False |
| 2 | False | True | True |
| 3 | True | False | True |
4. Creating a backup of dataset using dataframe.copy() function
Manipulating datasets can cause data loss; therefore, it is essential to create backup copies to avoid losing important information.
In Python, the dataframe.copy() function is a Pandas conversion function that creates a copy of the original dataset. Any modifications made to the copy will not affect the original dataset hence maintaining a reliable backup.
Here’s an example to demonstrate the function:
– Example: Creating a backup of dataset
“`
import pandas as pd
data = {‘Name’: [‘Ali’, ‘Ahmed’, ‘Neha’, ‘Sara’], ‘Age’: [21, 22, 23, 21], ‘Gender’: [‘M’, ‘M’, ‘F’, ‘F’]}
df = pd.DataFrame(data)
#creating backup of dataset
df_copy = df.copy()
#df_copy
print(df_copy)
“`
In this example, we create a backup copy of df using dataframe.copy() function and store it in df_copy. When df_copy is printed, it will contain the same information as df.
Conclusion
In this article, we demonstrated the four Pandas Conversion Functions: astype(), isna(), notna() and dataframe.copy(). We then went ahead and presented practical examples for each so that readers can understand concepts.
Now that you understand how each of these functions works, you can use them independently or together when performing data analysis or pre-processing tasks. Data management is vital in all data analysis projects, so it’s essential to use suitable tools such as Pandas conversion functions to efficiently and reliably handle data.
Data analysis and pre-processing tasks involve converting variables to different data types, ensuring datasets are free from NULL values and creating backup copies for future reference. In this digital era, data is being collected in huge volumes, and data analysis is becoming more critical to businesses and governments.
Effective data management is fundamental to ensuring that insights drawn from data analysis are accurate and reliable. This is where Pandas Conversion Functions come in.
Pandas, a popular Python library, offers advanced and flexible data structures and analysis tools to handle data efficiently. In this article, we discussed four primary Pandas Conversion Functions.
Let’s dive deeper into each function and learn how they’re used. 1.to Pandas astype() function
In Pandas, astype() function is the primary conversion function for altering the data type of a variable.
It is useful for converting dataset variables from default data types to specific data types based on project requirements. For instance, an analyst may need to change a categorical variable to integer to perform mathematical operations such as calculation of Mean or Median.
In this article, we introduced an example where astype() function is used to convert a column in a dataframe from the string data type to integer data type. As shown in the example, the astype() function can convert columns in a dataframe to other valid Python objects such as category and Boolean data types.
It helps users ensure efficient data analysis by ensuring they work with data types they are comfortable with. 2.
Checking for NULL values using isna() function
Handling missing or NULL values is crucial in data pre-processing. Missing values such as NULL can cause issues in data analysis as various Python-based libraries interpret NULL values differently.
Pandas offers the isna() function to address such issues. The isna() function returns a DataFrame or Series containing Boolean values, highlighting if a particular value is NULL or not.
It is useful for identifying whether a specific column in a dataset contains missing values. Once these values are identified, an analyst can decide to handle them by either removing them or filling them with other values such as the mean or median of the data.
We introduced an example where the function is used to determine the presence of NULL values in a dataset. Each row will show Boolean values for columns containing NULL values.
The isna() function offers a quick way for analysts to locate missing values in a dataset and deal with them appropriately. 3.
Segregating non-null values using notna() function
Analysts usually don’t want to delete the entire row or column containing null values since they may lose useful data. Instead, analysts can use the notna() function, which provides a Boolean output array highlighting columns with non-null or non-missing values.
In Pandas, the notna() function is used to locate the column containing non-null or non-missing values in a dataset. Generally, the Boolean value “False” is returned where the value in a given column is NULL, whereas the Boolean value “True” is returned when the value is non-null.
The function returns a DataFrame or Series containing Boolean values relative to values containing non-null and non-missing values. We introduced an example where notna() function is used to segregate non-null values in a dataset.
The example created an array containing the location of non-missing values. Afterward, analysts can manipulate the dataset by filling NULL values or removing the ones containing NULL values.
4. Creating a backup of the dataset using dataframe.copy() function
Data analysts should always have a backup of the original dataset before making any manipulations to the data.
Luckily, Pandas provides a dataframe.copy() function, which creates a copy of the original dataframe. The dataframe.copy() function is useful in cases where an analyst makes a wrong manipulation on the dataframe during an analysis project.
For instance, deleting the original column containing original values instead of a manipulated column. By making a copy of the original dataset, analysts ensure they can always revert back to the original dataset as it was before the analysis session began.
In this article, an example illustrated how the dataframe.copy() function creates a copy of the original dataset. Once copied, an analyst can comfortably manipulate the copy as they wish, secure in the knowledge that the original dataset’s integrity remains intact.
Conclusion
In conclusion, Pandas Conversion Functions are useful for handling data effectively in data analysis projects. As explained above, the four Pandas Conversion Functions introduced in this article – astype(), isna(), notna(), and dataframe.copy() – help analysts prepare data for analysis.
By using Pandas Conversion Functions to pre-process and manipulate datasets, analysts can rely on the data analysis insights they generate. They can pinpoint key trends and drivers behind business or government data to ensure effective decision-making.
In data analysis and pre-processing, manipulating datasets is critical, and it is essential to have reliable data management tools. Pandas Conversion Functions is a powerful tool for handling data effectively and efficiently.
In this article, we discussed the four primary Pandas Conversion Functions: astype(), isna(), notna(), and dataframe.copy(). We provided examples for each function to help readers understand how they work.
By using Pandas Conversion Functions, analysts can create accurate and useful insights. Takeaways include the importance of having backup copies of the original dataset, knowing how to handle NULL values, and selecting the right data type for better data manipulation.
Pandas is a popular library that analysts can use to deal with data and its different data types better.