Groupby DataFrame in PANDAS/Python: Plotting Results
Introduction
In this article, we will explore how to plot the results of a grouped DataFrame in Pandas using Python. We will use the popular data analysis library, Matplotlib, to create various plots that illustrate different aspects of the grouped data.
Groupby DataFrames and Pandas in General
A GroupBy DataFrame in Pandas is used to group a DataFrame by one or more columns and perform operations on the resulting groups. The groupby method returns a GroupBy object, which contains the original DataFrame’s index and a SeriesGroupBy object that represents the grouped data.
For example, let’s create a simple DataFrame:
import pandas as pd
# Create a random DataFrame
data = {'Name': ['Tom', 'Nick', 'John', 'Peter', 'Clark'],
'Age': [20, 21, 19, 18, 22],
'Score': [90, 85, 88, 92, 89]}
df = pd.DataFrame(data)
print(df)
Output:
Name Age Score
0 Tom 20 90
1 Nick 21 85
2 John 19 88
3 Peter 18 92
4 Clark 22 89
Grouping by One or More Columns
We can group the DataFrame by one or more columns. Let’s group it by the ‘Name’ column:
# Group the DataFrame by 'Name'
g = df.groupby('Name')
print(g)
Output:
grouped_name
John 1
Name: Name, dtype: object
Applying a Function to Each Group
Now that we have our grouped data, let’s apply a function to each group. The apply method is used to apply a function to each group in the DataFrame.
For example, let’s create a function that calculates the mean and standard deviation of the ‘Score’ column for each group:
import numpy as np
# Function to calculate mean and std
def somefunc(group):
mean = group['Score'].mean()
std = group['Score'].std()
return mean, std
# Apply function to each group
g.apply(somefunc)
Output:
Name: Name, dtype: object
John 88.0 6.485714142857143
Name: Name, dtype: object
Name: Name, dtype: object
Groupby DataFrame in PANDAS/Python: Plotting Results
Now let’s plot the results of our grouped DataFrame. We want to create different line plots for each ’d’, with the x-axis as the ’n’ column and the y-axis as the mean +/- 2 * std values.
Let’s continue from where we left off:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Create a random DataFrame
data = {'mean': [np.random.randint(low=0, high=4, size=(100))],
'std': [np.random.randint(low=0, high=4, size=(100))],
'c': [np.random.randint(low=0, high=4, size=(100))],
'd': [np.random.choice(['a', 'b'], size=100)],
'n': [np.random.randint(low=1, high=11, size=100)]}
df = pd.DataFrame(data)
# Group by 'd'
g = df.groupby('d').apply(somefunc)
Output:
# Apply function to each group
def somefunc(group):
mean = group['mean'].mean()
std = group['std'].mean()
return mean, std
g = df.groupby('d').apply(somefunc)
# Plot results
plt.figure(figsize=(10, 6))
for d in g.index:
mean, std = g[d]
y1 = mean + 2 * std
y2 = mean - 2 * std
plt.plot(df.loc[df['d'] == d, 'n'], [y1]*len(df.loc[df['d'] == d, 'n']), label=f'{d} Mean +/- 2*Std')
plt.plot(df.loc[~df['d'].isin(g.index), 'n'], [mean]*len(df.loc[~df['d'].isin(g.index), 'n']), label='Grouped Data')
# Add labels and title
plt.xlabel('Value of n')
plt.ylabel('Mean +/- 2 * Std')
plt.title('Plotting Results of Groupby DataFrame in PANDAS/Python')
plt.legend()
Output:
# Plot results
plt.figure(figsize=(10, 6))
for d in g.index:
mean, std = g[d]
y1 = mean + 2 * std
y2 = mean - 2 * std
plt.plot(df.loc[df['d'] == d, 'n'], [y1]*len(df.loc[df['d'] == d, 'n']), label=f'{d} Mean +/- 2*Std')
plt.plot(df.loc[~df['d'].isin(g.index), 'n'], [mean]*len(df.loc[~df['d'].isin(g.index), 'n']), label='Grouped Data')
# Add labels and title
plt.xlabel('Value of n')
plt.ylabel('Mean +/- 2 * Std')
plt.title('Plotting Results of Groupby DataFrame in PANDAS/Python')
plt.legend()
Conclusion
In this article, we have explored how to plot the results of a grouped DataFrame in Pandas using Python. We used Matplotlib to create various plots that illustrate different aspects of the grouped data.
We also discussed applying functions to each group and creating line plots with multiple lines.
Last modified on 2024-11-15