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Pandas is an open source data analysis library in Python. It provides many built-in methods to perform operations on numerical data.

Data visualization is very popular nowadays and is used to quickly analyze data visually. We can visualize the data in the Pandas data frame through a library called Matplotlib.

It provides a variety of charts to plot data.

Plotting a single line graph with data points in Pandas

A line graph is a type of graph that displays a series of data points called markers connected by straight lines. It represents the change in data points or trends over time.

In the following example, we have weather data. To plot the relationship between date and temperature, first, we have to list()convert the required columns into a list using the method.

With plot()the help of , we can draw a straight line graph by specifying xand .y

We can further decorate the plot by specifying lines color, linestyle, markerand . We also provide titles for both axes using the and methods.labelxlabel()ylabel()

To rotate the x labels, we use xticks()the method. For clarity, legend()the line labels are displayed with colors in the corners of the chart.

Finally, we call show()the method to display a line graph illustrating the relationship between date and temperature.

Sample code:

# Python 3.x
import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv("weather_data.csv")
display(df)
date = df["Date"]
temp = df["Temperature in Celcius"]
x = list(date)
y = list(temp)
plt.plot(x, y, color="g", linestyle="solid", marker="o", label="Temperature")
plt.xlabel("Date")
plt.xticks(rotation=25)
plt.ylabel("Temperature in Celcius")
plt.title("Temperature Data")
plt.legend()
plt.show()

Output:

Plotting Multiple Lines Using Data Points in Pandas

Suppose we want to visualize multiple attributes of a Pandas dataframe in a plot. In this case, we have to create many line plots, one for each line.

Each row has a different 颜色sum 标签. We show a line graph of temperature and humidity for each date in the following example.

Here, the legend helps distinguish the lines representing temperature and humidity.

Sample code:

# Python 3.x
import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv("weather_data.csv")
display(df)
date = df["Date"]
temp = df["Temperature in Celcius"]
humidity = df["Humidity in %"]
x = list(date)
y1 = list(temp)
y2 = list(humidity)
plt.plot(x, y1, color="g", linestyle="solid", marker="o", label="Temperature")
plt.plot(x, y2, color="b", linestyle="solid", marker="o", label="Humidity")
plt.xlabel("Date")
plt.xticks(rotation=25)
plt.title("Temperature and Humidity Data")
plt.legend()
plt.show()

Output: