How to Plot Mean and Standard Deviation in Pandas?

23 Jul, 2021
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 the necessary libraries to read# dataset and work on thatimport pandas as pdimport numpy as npimport matplotlib.pyplot as plt # Make the dataframe for evaluation on Errorbarsdf = 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)
  
Output:
 
Sample DataFrame
 
groupby the subplots with mean and std to get error bars: 
Python3
# Subplots as having two types of qualityfig, ax = plt.subplots() for key, group in df.groupby('quality'):    group.plot('insert', 'mean', yerr='std',               label=key, ax=ax) plt.show()
  Output:
 
Example 1: ErrorBar with group-plot
 
Now we see error bars using NumPy keywords of mean and std:
Python3
# Groupby the quality column using aggregate# value of mean and stdqual = 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')
  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
# reading the datasetdf = pd.read_csv('Toast.csv')df_prices = df.groupby("type").agg([np.mean, np.std])
  
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'] # checking for resultsprices.head()
  Output:
 
Result: the aggregate value of  groupby()
 
Errorbar using Mean:
Python3
prices.plot(kind = "barh", y = "mean", legend = False,            title = "Average Prices")
  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")
Output:
Example 4: Errorbar with Std
Errorbars are more obstacles.
They are easy to execute with good estimation values.
Relatively uniform because of complex interpretation power with a data frame.
surindertarika1234
Data Visualization
Python-matplotlib
Python
Writing code in comment?
Please use ide.geeksforgeeks.org,
generate link and share the link here.
How to Install PIP on Windows ?
Python Classes and Objects
Python | os.path.join() method
Introduction To PYTHON
Python OOPs Concepts
How to drop one or multiple columns in Pandas Dataframe
How To Convert Python Dictionary To JSON?
Check if element exists in list in Python
Python | Get unique values from a list
Python | datetime.timedelta() function