# Uncovering Insights: Plotting Histograms by Group with Pandas DataFrame

Are you interested in analyzing data patterns and distributions in your pandas DataFrame? One common and informative method is plotting histograms based on groups within your data.

This can help you visually identify underlying trends and patterns that you might have missed otherwise. In this article, we’ll discuss two different approaches to plot histograms by group using pandas DataFrame: using multiple plots and one plot.

We’ll also provide helpful examples to illustrate each approach.

## Method 1: Plotting Histograms by Group Using Multiple Plots

The first approach involves plotting histograms by group using multiple plots.

This means that you will create multiple histograms, one for each group, and display them together. To achieve this, you can use Matplotlib’s subplot function.

### Creating histograms by team

Let’s say that you have a DataFrame containing NBA team names and their corresponding points scored in a game. You want to create a histogram for each team to visualize the distribution of their points scored.

To do this, you can first group the data by team, and then apply the hist method to each group. Here’s the code to achieve this:

``````import pandas as pd
import matplotlib.pyplot as plt
# create sample data
data = {'Team': ['Lakers', 'Lakers', 'Lakers', 'Celtics', 'Celtics', 'Heat', 'Heat'],
'Points': [100, 110, 105, 90, 80, 95, 100]}
df = pd.DataFrame(data)
# group data by team
groups = df.groupby('Team')
# create a histogram for each group
fig, axs = plt.subplots(len(groups), 1, figsize=(6, 6))
for i, (name, group) in enumerate(groups):
axs[i].hist(group['Points'])
axs[i].set_title(name)
``````

The code first creates a sample DataFrame with team names and their corresponding points scored. It then groups the data by team using the groupby method.

The next line creates a figure with multiple subplots based on the number of groups. For each group, a histogram of their points is created and added to its corresponding subplot.

### Customizing histograms with edgecolor and figsize

You can also customize your histograms by adding edgecolor and specifying the figsize. Here’s an updated code that includes these customizations:

``````import pandas as pd
import matplotlib.pyplot as plt
# create sample data
data = {'Team': ['Lakers', 'Lakers', 'Lakers', 'Celtics', 'Celtics', 'Heat', 'Heat'],
'Points': [100, 110, 105, 90, 80, 95, 100]}
df = pd.DataFrame(data)
# group data by team
groups = df.groupby('Team')
# create a histogram for each group
fig, axs = plt.subplots(len(groups), 1, figsize=(6, 10))
for i, (name, group) in enumerate(groups):
axs[i].hist(group['Points'], edgecolor='black')
axs[i].set_title(name)
plt.tight_layout()
``````

This updated code adds the edgecolor argument to the hist method, which sets the color of the histogram edges to black. It also increases the figsize to (6, 10) so that you have more space to display your histograms.

Finally, the tight_layout method improves the layout of the plots by automatically adjusting the padding between subplots.

## Method 2: Plotting Histograms by Group Using One Plot

The second approach involves plotting histograms by group using one plot.

This means that you will create a single histogram with one bar per group, each of which will be color-coded to identify different groups.

### Creating histograms by team

To achieve this, you can use Matplotlib’s bar function. Here’s the code to create a histogram by team using one plot:

``````import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# create sample data
data = {'Team': ['Lakers', 'Lakers', 'Lakers', 'Celtics', 'Celtics', 'Heat', 'Heat'],
'Points': [100, 110, 105, 90, 80, 95, 100]}
df = pd.DataFrame(data)
# group data by team
groups = df.groupby('Team')
# create a histogram with one bar per group
fig, ax = plt.subplots(figsize=(6, 6))
colors = ['blue', 'red', 'green']
for i, (name, group) in enumerate(groups):
ax.hist(group['Points'], alpha=0.5, color=colors[i], label=name)
ax.legend()
``````

The code first creates a sample DataFrame with team names and their corresponding points scored. It then groups the data by team using the groupby method.

The next line creates a figure with a single subplot. For each group, a histogram of their points is added to the same subplot using the hist method.

The colors argument sets the color for each bar based on the group.

### Customizing histograms with edgecolor and figsize

You can also customize your histogram in this approach. Here’s an updated code that includes customization:

``````import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# create sample data
data = {'Team': ['Lakers', 'Lakers', 'Lakers', 'Celtics', 'Celtics', 'Heat', 'Heat'],
'Points': [100, 110, 105, 90, 80, 95, 100]}
df = pd.DataFrame(data)
# group data by team
groups = df.groupby('Team')
# create a histogram with one bar per group
fig, ax = plt.subplots(figsize=(6, 6))
colors = ['blue', 'red', 'green']
for i, (name, group) in enumerate(groups):
ax.hist(group['Points'], alpha=0.5, color=colors[i], edgecolor='white', linewidth=2, label=name)
ax.set_xlabel('Points')
ax.set_ylabel('Count')
ax.set_title('Points Distribution by Team')
ax.legend()
plt.show()
``````

This updated code adds edgecolor, linewidth, xlabel, ylabel, and title arguments to customize your histogram. You can set xlabel to a descriptive label such as ‘Points,’ ylabel to ‘Count,’ and title to a descriptive name, such as ‘Points Distribution by Team.’

## Conclusion

Plotting histograms by group in pandas DataFrame is a useful technique to visualize distributions and identify underlying trends and patterns. Using multiple plots and one plot has its own advantages and can be customized to fit your requirements.

With the help of code examples, we have shown how to plot histograms by group using two different methods, Multiple Plots and One Plot, while customizing its view with different parameters. Use this knowledge to your benefit and visualize your data better.

In our previous article, we discussed how to plot histograms by group using multiple plots and one plot, along with examples. In this expansion, we’ll dive deeper into the second approach and explore how to create overlaid histograms for multiple teams using one plot.

Overlaid histograms are a useful tool to compare the distribution of data between multiple groups. This is especially relevant when you have data for multiple teams and you want to compare their performance using the same metric.

The histograms are overlaid on top of each other, so you can easily compare their shapes and relative positions.

### Creating overlaid histograms for multiple teams

Suppose you have a dataset containing the sales figures for multiple teams in your organization. To create overlaid histograms of their sales figures, you can use the same code as in the previous article.

However, we will make some modifications to plot the histograms on top of each other.

``````import pandas as pd
import matplotlib.pyplot as plt
# create sample data
data = {'Team1': [100, 110, 105, 90, 80, 95, 100],
'Team2': [70, 75, 80, 85, 90, 95, 80],
'Team3': [50, 55, 60, 65, 70, 75, 80]}
df = pd.DataFrame(data)
# plot overlaid histograms
fig, ax = plt.subplots(figsize=(8, 6))
df.plot.hist(ax=ax, alpha=0.5, bins=10)
ax.set_xlabel('Sales figures')
ax.set_ylabel('Count')
ax.set_title('Sales Figures by Team')
plt.show()
``````

The code first creates a sample dataset with sales figures for three teams. Then we create a figure with a single subplot and use the pandas plot.hist method to plot the histograms on the same plot.

The alpha argument sets the transparency level for the bars, so when they are overlaid, you can see the shape of the histogram more clearly. We also set the bins argument to 10 to get a better resolution of the data, and set xlabel, ylabel, and title to descriptive labels.

Finally, we call plt.show() to display the plot.

### Customizing overlaid histograms using alpha

You can also customize your histograms by adjusting the transparency level of each histogram individually. Here is an updated code to achieve this:

``````import pandas as pd
import matplotlib.pyplot as plt
# create sample data
data = {'Team1': [100, 110, 105, 90, 80, 95, 100],
'Team2': [70, 75, 80, 85, 90, 95, 80],
'Team3': [50, 55, 60, 65, 70, 75, 80]}
df = pd.DataFrame(data)
# plot overlaid histograms
fig, ax = plt.subplots(figsize=(8, 6))
alpha_values = [0.5, 0.7, 0.9]
for i, team in enumerate(df.columns):
df[team].plot.hist(ax=ax, alpha=alpha_values[i], bins=10)
ax.set_xlabel('Sales figures')
ax.set_ylabel('Count')
ax.set_title('Sales Figures by Team')
ax.legend()
plt.show()
``````

This updated code introduces a new alpha_values list, which stores transparency values that we can use to adjust the histograms’ color. The for loop iterates through each team and sets its transparency value to the corresponding value in the alpha_values list.

We also included a legend that displays the team name under each histogram.

## Conclusion

Creating overlaid histograms is a great way to compare the distribution of data between multiple groups. By using the same plot and overlapping histograms, you can easily compare the shape, location, and relative sizes of the data.

The transparency level of each histogram can be adjusted to reveal more detail or focus on certain areas of interest. In this expanding section, we have discussed how to create overlaid histograms for multiple teams using one plot in detail.

The code examples provided, along with explanations, should help you apply this technique to your work and create compelling and informative visualizations of your data. Plotting histograms by group in pandas DataFrame is a valuable technique that can help you identify patterns and trends in your dataset.

Our article has explored two methods of creating histograms by group using one plot or multiple plots. We have also demonstrated through examples how to customize histograms with parameters such as edgecolor, alpha, figsize, and bin size.

Finally, we focused on creating overlaid histograms for multiple teams using one plot and how to customize them. These techniques can help you create informative data visualizations and communicate your findings effectively.

In conclusion, plotting histograms by group is a powerful tool that can lead to valuable insights that you might have otherwise missed.