**Matplotlib Tutorial in Python**

**Python Histograms | Chapter 6 **

A **Python Histogram****/Matplotlib Histogram** is an accurate representation of the distribution of numerical data. Bar graphsrelates to two variables, however a Python Histogram relates to only one variable and shows it distribution in different range of the values called “bin” or “bucket”.

**Python Histogram in Matplotlib**

To create as simple Python Histogram in Matplotlib we will be using plt.hist(). Let us quickly right the following code to plot a simple Python/Matplolib Histogram of ages in Matplotlib.

```
# python_histogram.py
import matplotlib.pyplot as plt
plt.style.use('ggplot')
ages = [12, 13, 15, 19, 20, 20, 24, 26, 32, 39, 49]
plt.hist(ages)
plt.xlabel("Age")
plt.ylabel("Age Disribution")
plt.title("Age Distribution Histogram")
plt.show()
```

**Python Histogram with bins in Matplotlib**

The above Python Histogram is an-auto Python Histogram created by the Matplolib. Now we will define the bins and pass it to plt.hist() to create the Matplotlib Histogram. We can simply choose the number of bins and Matplotlib will distribute the data on the basis of the number of bins provided in the Python Histogram. We will also add border to each bin so that we can distinguish between them easily.

```
# python_histogram.py
bins = 5
plt.hist(ages, bins = bins, edgecolor = 'black')
```

So, in the above Matplotlib/Python Histogram, the age-range is evenly distributed between 5 bins and each bin has certain number of persons corresponding to that age range.

Instead of Matplotlib doing the distribution in the Histogram, we can also provide the age-range in form of a list.

```
# python_histogram.py
ages = [12, 13, 15, 19, 20, 20, 24, 26, 32, 39, 49]
bins = [10, 15, 20, 25, 30, 35, 40, 45, 50]
plt.hist(ages, bins=bins, edgecolor='black')
```

.

So, here instead of providing number of bins, we have provided the list, where two adjacent items represent one bin of that age-group. So, we have 8 age-groups and the Python Histrogram shows the distribution of person in those 8 age groups. We can also, ignore certain ages by just removing them from the bin-range.

```
# python_histogram.py
ages = [12, 13, 15, 19, 20, 20, 24, 26, 32, 39, 49]
bins = [20, 25, 30, 35, 40, 45, 50]
plt.hist(ages, bins=bins, edgecolor='black')
```

So, here we have removed two age-bins (age-ranges) i.e. 10-15 and 15-20. Though 12,13,15 are present in the ages but still they are not plotted on the histogram.

**Customizing the Python Histogram in Matplotlib**

We can further customize our Histograms, like we can draw a verticle line for median age using plt.axvline().

```
# python_histogram.py
import matplotlib.pyplot as plt
plt.style.use('ggplot')
ages = [12, 13, 15, 19, 20, 20, 24, 26, 32, 39, 49]
bins = [10, 15, 20, 25, 30, 35, 40, 45, 50]
median_age = 20
plt.hist(ages, bins=bins, edgecolor='black')
plt.axvline(median_age, color='blue', label="Median Age")
plt.xlabel("Age")
plt.ylabel("Age Disribution")
plt.title("Age Distribution Histogram")
plt.legend()
plt.show()
```

**Table of Contents of Matplotlib Tutorial in Python**

**Matplotlib Tutorial in Python | Chapter 1 | Introduction**

**Matplotlib Tutorial in Python | Chapter 2 | Extracting Data from CSVs and plotting Bar Charts**

**Pie Charts in Python | Matplotlib Tutorial in Python | Chapter 3**

**Matplotlib Stack Plots/Bars | Matplotlib Tutorial in Python | Chapter 4**

**Filling Area on Line Plots | Matplotlib Tutorial in Python | Chapter 5**

**Python Histograms | Matplotlib Tutorial in Python | Chapter 6**

**Scatter Plotting in Python | Matplotlib Tutorial | Chapter 7**

**Plot Time Series in Python | Matplotlib Tutorial | Chapter 8**

**Python Realtime Plotting | Matplotlib Tutorial | Chapter 9**

**Matplotlib Subplot in Python | Matplotlib Tutorial | Chapter 10**

**Python Candlestick Chart | Matplotlib Tutorial | Chapter 11**

So, here we have a median line of blue color at age 30.

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In the next chapter we will be learning to create scatterplots in Matplotlib in Python.

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