Interpolating missing values in Pandas can be a useful technique to fill in any gaps in your data set. With the use of the interpolate() function, you can easily fill in missing data points and visualize the updated data set to gain a better understanding of your data.
To demonstrate this technique, let’s start by creating a simple Pandas DataFrame with some missing values. We will create a sales DataFrame with monthly sales data for a few products.
“`python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Create sales DataFrame with missing values
sales = pd.DataFrame({
‘month’: [‘January’, ‘February’, ‘March’, ‘April’, ‘May’, ‘June’, ‘July’, ‘August’, ‘September’, ‘October’, ‘November’, ‘December’],
‘product_a’: [100, 150, np.nan, 200, 250, 300, np.nan, 350, np.nan, 400, 450, 500],
‘product_b’: [450, np.nan, 550, 600, 650, 700, np.nan, 750, 800, 850, np.nan, 900],
‘product_c’: [1000, 1100, 1200, np.nan, 1300, 1400, 1500, 1600, 1700, np.nan, 1800, 1900]
})
# View sales DataFrame
print(sales)
“`
Output:
“`
month product_a product_b product_c
0 January 100.0 450.0 1000.0
1 February 150.0 NaN 1100.0
2 March NaN 550.0 1200.0
3 April 200.0 600.0 NaN
4 May 250.0 650.0 1300.0
5 June 300.0 700.0 1400.0
6 July NaN NaN 1500.0
7 August 350.0 750.0 1600.0
8 September NaN 800.0 1700.0
9 October 400.0 850.0 NaN
10 November 450.0 NaN 1800.0
11 December 500.0 900.0 1900.0
“`
As you can see, there are missing values in the DataFrame. To visualize this data, we can use a line chart to see the sales trends for each product over the year.
“`python
# Visualize sales DataFrame with line chart
plt.plot(sales[‘month’], sales[‘product_a’], label=’Product A’)
plt.plot(sales[‘month’], sales[‘product_b’], label=’Product B’)
plt.plot(sales[‘month’], sales[‘product_c’], label=’Product C’)
plt.legend()
plt.xticks(rotation=45)
plt.title(‘Sales Trends’)
plt.xlabel(‘Month’)
plt.ylabel(‘Sales’)
plt.show()
“`
Output:
From the chart, we can see that there are missing data points for all three products. To fill in these missing data points, we can use the interpolate() function.
“`python
# Interpolate missing values in sales DataFrame
sales.interpolate(inplace=True)
# View updated sales DataFrame
print(sales)
“`
Output:
“`
month product_a product_b product_c
0 January 100.0 450.0 1000.0
1 February 150.0 500.0 1100.0
2 March 175.0 550.0 1200.0
3 April 200.0 600.0 1250.0
4 May 250.0 650.0 1300.0
5 June 300.0 700.0 1400.0
6 July 325.0 725.0 1500.0
7 August 350.0 750.0 1600.0
8 September 375.0 800.0 1700.0
9 October 400.0 850.0 1750.0
10 November 450.0 875.0 1800.0
11 December 500.0 900.0 1900.0
“`
The interpolate() function has filled in the missing data points by using linear interpolation. This means that the data points were filled in with values that fall between the known data points, creating a straight line between them.
Now, we can visualize the updated sales DataFrame with another line chart. “`python
# Visualize updated sales DataFrame with line chart
plt.plot(sales[‘month’], sales[‘product_a’], label=’Product A’)
plt.plot(sales[‘month’], sales[‘product_b’], label=’Product B’)
plt.plot(sales[‘month’], sales[‘product_c’], label=’Product C’)
plt.legend()
plt.xticks(rotation=45)
plt.title(‘Sales Trends’)
plt.xlabel(‘Month’)
plt.ylabel(‘Sales’)
plt.show()
“`
Output:
As you can see, the missing values have been filled in and the line chart displays a smoother curve for each product.
This allows us to better analyze the sales trends for each product over the year. In conclusion, interpolating missing values in Pandas can be a useful technique for filling in gaps in your data set.
By using the interpolate() function, you can easily fill in missing data points and visualize the updated data set to gain a better understanding of your data. Whether you are working with sales data, survey data, or any other type of data, interpolating missing values can help you make more informed decisions based on your data.
Interpolating missing values in Pandas using the interpolate() function can be a useful technique to fill in gaps in your data set. By using this function, you can easily fill in missing data points and visualize the updated data set to gain a better understanding of your data.
In this article, we created a sales DataFrame with missing values and used the interpolate() function to fill in those missing values, visualizing the updated data set through a line chart. Interpolating missing values can help you make more informed decisions based on your data, whether you are working with sales data, survey data, or any other type of data.
Remember to employ this technique when dealing with missing values to optimize your data analysis.