How to Plot Mean and Standard Deviation in Pandas?
Errorbar is the plotted chart that refers to the errors contained in the data frame, which shows the confidence & precision in a set of measurements or calculated values. Error bars help in showing the actual and exact missing parts as well as visually display the errors in different areas in the data frame. Error bars are the descriptive behavior that holds information about the variances in data as well as advice to make proper changes to build data more insightful and impactful for the users.
Here we discuss how we plot errorbar with mean and standard deviation after grouping up the data frame with certain applied conditions such that errors become more truthful to make necessary for obtaining the best results and visualizations.
Modules Needed:
pip install numpy
pip install pandas
pip install matplotlib
Here is the DataFrame from which we illustrate the errorbars with mean and std:
Python3
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({
'insert' : [ 0.0 , 0.1 , 0.3 , 0.5 , 1.0 ],
'mean' : [ 0.009905 , 0.45019 , 0.376818 , 0.801856 , 0.643859 ],
'quality' : [ 'good' , 'good' , 'poor' , 'good' , 'poor' ],
'std' : [ 0.003662 , 0.281895 , 0.306806 , 0.243288 , 0.322378 ]})
print (df)
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Output:
Sample DataFrame
groupby the subplots with mean and std to get error bars:
Python3
fig, ax = plt.subplots()
for key, group in df.groupby( 'quality' ):
group.plot( 'insert' , 'mean' , yerr = 'std' ,
label = key, ax = ax)
plt.show()
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Output:
Example 1: ErrorBar with group-plot
Now we see error bars using NumPy keywords of mean and std:
Python3
qual = df.groupby( "quality" ).agg([np.mean, np.std])
qual = qual[ 'insert' ]
qual.plot(kind = "barh" , y = "mean" , legend = False ,
xerr = "std" , title = "Quality" , color = 'green' )
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Output:
Example 2: ErrorBar with Bar Plot
By the above example, we can see that errors in poor quality are higher than good instead of more good values in the data frame.
Now, we move with another example with data frame below:
Dataset – Toast
By the above data frame, we have to manipulate this data frame to get the errorbars by using the ‘type’ column having different prices of the bags. To manipulation and perform calculations, we have to use a df.groupby function that has a prototype to check the field and execute the function to evaluate result.
We are using two inbuilt functions of mean and std:
df.groupby("col_to_group_by").agg([func_1, func_2, func_3, .....])
Python3
df = pd.read_csv( 'Toast.csv' )
df_prices = df.groupby( "type" ).agg([np.mean, np.std])
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As we have to evaluate the average price, so apply this groupby on ‘AveragePrice’. Also, check the result of prices and with the visualization display the errorbars
Python3
prices = df_prices[ 'AveragePrice' ]
prices.head()
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Output:
Result: the aggregate value of groupby()
Errorbar using Mean:
Python3
prices.plot(kind = "barh" , y = "mean" , legend = False ,
title = "Average Prices" )
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Output:
Example 3: Errorbar with Mean
By the above visualization, it’s clear that organic has a higher mean price than conventional.
Errorbar using Standard Deviation (std):
Python3
prices.plot(kind = "barh" , y = "mean" , legend = False ,
title = "Average Prices" , xerr = "std" )
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Output:
Example 4: Errorbar with Std
Advantages of Errorbars:
- Errorbars are more obstacles.
- They are easy to execute with good estimation values.
- Relatively uniform because of complex interpretation power with a data frame.
Last Updated :
23 Jul, 2021
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