Data visualization is the representation of data or information in a graph, chart, or other visual formats. It communicates the relationships of the data with images.

Data visualization is used in a large number of areas in statistics and machine learning.

There are five key plots that you need to know well for basic data visualization. They are:

Line Plot

Bar Chart

Histogram Plot

Box and Whisker Plot

Pie chart

Scatter chart

Series chart

Mosaic chart

Heat Map

__Line Plot__

__Line Plot__

A line plot is generally used to present observations collected at regular intervals.

The x-axis represents the regular interval, such as time. The y-axis shows the observations, ordered by the x-axis and connected by a line.

A line plot can be created by calling the plot() function and passing the x-axis data for the regular interval, and y-axis for the observations.

Line plot is a type of chart that displays information as a series of data points connected by straight line segments.

Line plots are generally used to visualize the directional movement of one or more data over time. In this case, the X axis would be DateTime and the y axis contains the measured quantity, like, stock price, weather, monthly sales, etc.

**# create line plot****
****pyplot.plot(x, y)**

__Bar Chart__

__Bar Chart__

A bar chart or bar graph is a chart or graph that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally.

__Draw vertically:__

__Example:__

```
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
objects = ('red', 'green', 'yellow', 'blue', 'orange', 'pink')
y_pos = np.arange(len(objects))
performance = [15,12,10,5,4,1]
plt.barh(y_pos, performance, align='center', alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('Value')
plt.title('Color usage')
plt.show()
```

__Output:__

__Draw horizontally:__

To draw horizontally used the function **barh()**

__Example:__

```
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
objects = ('red', 'green', 'yellow', 'blue', 'orange', 'pink')
y_pos = np.arange(len(objects))
performance = [15,12,10,5,4,1]
plt.barh(y_pos, performance, align='center', alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('Value')
plt.title('Color usage')
plt.show()
```

__Output:__

__Histogram plot:__

__Histogram plot:__

A histogram shows the frequency on the vertical axis and the horizontal axis is another dimension. Usually, it has bins, where every bin has a minimum and maximum value. Each bin also has a frequency between x and infinite.

__Example:__

```
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
x = [15,18,17,2,4,3,55,8,9,40,61,12,33,22,35,36,36,14,46,45]
num_bins = 5
n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)
plt.show()
```

__Output:__

__Box and Whisker Plot__

__Box and Whisker Plot__

A box plot which is also known as a whisker plot displays a summary of a set of data containing the minimum, first quartile, median, third quartile, and maximum.

__Drawing a Box Plot__

Boxplot can be drawn calling Series.box.plot() and DataFrame.box.plot(), or DataFrame.boxplot() to visualize the distribution of values within each column.

__Example__

```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(15, 5), columns=['Box1', 'Box2', 'Box3', 'Box4', 'Box5'])
df.plot.box(grid='True')
```

__Output:__

__Pie Chart__

__Pie Chart__

__Matplotlib pie chart__

First import matplotlib as:

*import matplotlib.pyplot as plt*

__Example:__

```
import matplotlib.pyplot as plt
# Data to plot
labels = 'color1', 'color2', 'color3', 'color4'
sizes = [115, 110, 280, 230]
colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
explode = (0.2, 0, 0, 0) # explode 1st slice
# Plot
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
autopct='%1.2f%%', shadow=True, startangle=180)
plt.axis('equal')
plt.show()
```

__Output:__

__With “Legend”__

```
import matplotlib.pyplot as plt
labels = ['green', 'yello', 'other', 'red']
sizes = [45, 20, 30, 25]
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90)
plt.legend(patche
s, labels, loc="best")
plt.axis('equal')
plt.tight_layout()
plt.show()
```

Output:

__Scatter Plot__

__Scatter Plot__

Use the __scatter()__ method to draw a scatter plot diagram:

__Example:__

```
import matplotlib.pyplot as plt
x = [2,1,10,8,5,15]
y = [45,54,56,55,110,78]
plt.scatter(x, y)
plt.show()
```

__Output:__

__Series chart__

__Series chart__

There are many ways to draw the time-series graph:

Line Plots.

Histograms and Density Plots.

Box and Whisker Plots.

Heat Maps.

Lag Plots or Scatter Plots.

Autocorrelation Plots.

__Mosaic chart __

__Mosaic chart__

These charts are a good representation of categorical entries. A mosaic plot allows visualizing multivariate categorical data in a rigorous and informative way.

__ Example__

```
from statsmodels.graphics.mosaicplot import mosaic
import matplotlib.pyplot as plt
import pandas
gender = ['male', 'male', 'male', 'female', 'female', 'female']
pet = ['cat', 'dog', 'dog', 'cat', 'dog', 'cat']
data = pandas.DataFrame({'gender': gender, 'pet': pet})
mosaic(data, ['pet', 'gender'])
plt.show()
```

__Output:__

__Heat Map__

__Heat Map__

It shows the 2D representation of data.

__Example:__

```
import numpy as np
import matplotlib.pyplot as plt
data = np.random.random((8, 8))
plt.imshow(data, cmap='cool', interpolation='nearest')
plt.show()
```

imshow(), function use to draw the heat map

__Output:__

Contact us for this machine learning assignment Solutions by Codersarts Specialist who can help you mentor and guide for such machine learning assignments.

If you have project or assignment files, You can send at __contact@codersarts.com__** **directly

## Comments