A bar plot or a bar chart is a type of data visualisation that represents data in the form of rectangular bars, with lengths or heights proportional to the values and data which they represent. The bar plots can be plotted both vertically and horizontally.

It is one of the most widely used type of data visualisation as it is easy to interpret and is pleasing to the eyes.

Matplotlib provides a very easy and intuitive method to create highly customized bar plots.

Before creating bar plots in matplotlib you must ensure that you have Python as well as Matplotlib installed on your system.

A very basic Bar Plot can be created with `bar()` method in `matplotlib.pyplot`

import matplotlib.pyplot as plt

x = ["A", "B", "C", "D"]

y = [2, 7, 9, 11]

plt.bar(x,y)

plt.show() # Shows the plot

When executed, this would show the following bar plot:


The `bar()` function takes arguments that describes the layout of the bars.

Here, `plt.bar(x,y)` is used to specify that the bar chart is to be plotted by taking the `x` array as X-axis and `y` array as Y-axis. You can customize the graph further like adding labels to the axes, color of the bars, etc. These will be explored in the upcoming sections.

Additionally, you can also use `numpy` arrays for faster generation when handling large datasets.

import matplotlib.pyplot as plt

import numpy as np

x = np.array(["A", "B", "C", "D"])

y = np.array([2, 7, 9, 11])

plt.bar(x,y)

plt.show()

Its output would be the same as above.

For creating customized bar plots, it is **highly recommended** to create the plots using `matplotlib.pyplot.subplots()`, otherwise it is difficult to apply the customizations in the newer versions of Matplotlib.

Let us create an imaginary graph of number of cars sold in a various years.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

years = ['1999', '2000', '2001', '2002']

num_of_cars_sold = [300, 500, 700, 1000]

ax.bar(years, num_of_cars_sold)

ax.set_xlabel("Years")

ax.set_ylabel("Number of cars sold")

ax.set_title("Number of cars sold in various years")

plt.show()


Here, we have created a `matplotlib.pyplot.subplots()` object which returns a `Figure` object `fig` as well as an `Axes` object `ax` both of which are used for customizing the bar plot. `ax.set_xlabel`, `ax.set_ylabel` and `ax.set_title` are respectively used for adding labels of X, Y axis and adding title to the graph.

Let us consider our previous example of number of cars sold in various years and suppose that we want to add different colors to the bars from different centuries and respective legends for better interpretation.

This can be achieved by creating two separate arrays `bar_colors` for bar colors and `bar_labels` for legend labels and passing them as arguments to parameters color and label respectively in `ax.bar` method.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

years = ['1998', '1999', '2000', '2001', '2002']

num_of_cars_sold = [200, 300, 500, 700, 1000]

bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue']

bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s']

ax.bar(years, num_of_cars_sold, color=bar_colors, label=bar_labels)

ax.set_xlabel("Years")

ax.set_ylabel("Number of cars sold")

ax.set_title("Number of cars sold in various years")

ax.legend(title='Centuries')

plt.show()


Note that the labels with a preceding underscore won't show up in the legend. Legend titles can be added by simply passing `title` argument in `ax.legend()`, as shown. Also, you can have a different color for all the bars by passing the `HEX` value of that color in the `color` parameter.

We may want to add labels to bars representing their absolute (or truncated) values for instant and accurate reading. This can be achieved by passing the `BarContainer` object (returned by `ax.bar()` method) which is basically a aontainer with all the bars and optionally errorbars to `ax.bar_label` method.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

years = ['1998', '1999', '2000', '2001', '2002']

num_of_cars_sold = [200, 300, 500, 700, 1000]

bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue']

bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s']

bar_container = ax.bar(years, num_of_cars_sold, color=bar_colors, label=bar_labels)

ax.set_xlabel("Years")

ax.set_ylabel("Number of cars sold")

ax.set_title("Number of cars sold in various years")

ax.legend(title='Centuries')

ax.bar_label(bar_container)

plt.show()


**Note:** There are various other methods of adding bar labels in matplotlib.

We can create horizontal bar plots by using the `barh()` method in `matplotlib.pyplot`. All the relevant customizations are applicable here also.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10,5)) # figsize is used to alter the size of figure

years = ['1998', '1999', '2000', '2001', '2002']

num_of_cars_sold = [200, 300, 500, 700, 1000]

bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue']

bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s']

bar_container = ax.barh(years, num_of_cars_sold, color=bar_colors, label=bar_labels)

ax.set_xlabel("Years")

ax.set_ylabel("Number of cars sold")

ax.set_title("Number of cars sold in various years")

ax.legend(title='Centuries')

ax.bar_label(bar_container)

plt.show()


We can also invert the Y-axis labels here to show the top values first.

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10,5)) # figsize is used to alter the size of figure

years = ['1998', '1999', '2000', '2001', '2002']

num_of_cars_sold = [200, 300, 500, 700, 1000]

bar_colors = ['tab:green', 'tab:green', 'tab:blue', 'tab:blue', 'tab:blue']

bar_labels = ['1900s', '_1900s', '2000s', '_2000s', '_2000s']

bar_container = ax.barh(years, num_of_cars_sold, color=bar_colors, label=bar_labels)

ax.set_xlabel("Years")

ax.set_ylabel("Number of cars sold")

ax.set_title("Number of cars sold in various years")

ax.legend(title='Centuries')

ax.bar_label(bar_container)

ax.invert_yaxis()

plt.show()


- [subplots in Matplotlib](matplotlib-sub-plot.md)

- [Pie Charts in Plotly](plotly-pie-charts.md)

The `plt.subplots()` function is a versatile and easy way to create a grid of subplots. It returns a figure and an array of Axes objects.

1. **Import Libraries**:

```python

import matplotlib.pyplot as plt

import numpy as np

```

2. **Generate Sample Data**:

```python

x = np.linspace(0, 10, 100)

y1 = np.sin(x)

y2 = np.cos(x)

y3 = np.tan(x)

```

3. **Create Subplots**:

```python

fig, axs = plt.subplots(3, 1, figsize=(8, 12))

```

- `3, 1` indicates a 3-row, 1-column grid.

- `figsize` specifies the overall size of the figure.

4. **Plot Data**:

```python

axs[0].plot(x, y1, 'r')

axs[0].set_title('Sine Function')

axs[1].plot(x, y2, 'g')

axs[1].set_title('Cosine Function')

axs[2].plot(x, y3, 'b')

axs[2].set_title('Tangent Function')

```

5. **Adjust Layout and Show Plot**:

```python

plt.tight_layout()

plt.show()

```

The result will be a figure with three vertically stacked subplots.


The `plt.subplot()` function allows you to add a single subplot at a time to a figure.

1. **Import Libraries and Generate Data** (same as above).

2. **Create Figure and Subplots**:

```python

plt.figure(figsize=(8, 12))

plt.subplot(3, 1, 1)

plt.plot(x, y1, 'r')

plt.title('Sine Function')

plt.subplot(3, 1, 2)

plt.plot(x, y2, 'g')

plt.title('Cosine Function')

plt.subplot(3, 1, 3)

plt.plot(x, y3, 'b')

plt.title('Tangent Function')

```

3. **Adjust Layout and Show Plot** (same as above).

The result will be similar to the first method but created using individual subplot commands.


`GridSpec` allows for more complex subplot layouts.

1. **Import Libraries and Generate Data** (same as above).

2. **Create Figure and GridSpec**:

```python

from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(8, 12))

gs = GridSpec(3, 1, figure=fig)

```

3. **Create Subplots**:

```python

ax1 = fig.add_subplot(gs[0, 0])

ax1.plot(x, y1, 'r')

ax1.set_title('Sine Function')

ax2 = fig.add_subplot(gs[1, 0])

ax2.plot(x, y2, 'g')

ax2.set_title('Cosine Function')

ax3 = fig.add_subplot(gs[2, 0])

ax3.plot(x, y3, 'b')

ax3.set_title('Tangent Function')

```

4. **Adjust Layout and Show Plot** (same as above).

The result will again be three subplots in a vertical stack, created using the flexible `GridSpec`.


- **`plt.subplots()`**: Creates a grid of subplots with shared axes.

- **`plt.subplot()`**: Adds individual subplots in a figure.

- **`GridSpec`**: Allows for complex and custom subplot layouts.

By mastering these techniques, you can create detailed and organized visualizations, enhancing the clarity and comprehension of your data presentations.