The main problem with seasonal plots is that they can be misleading. For example, if you plot the temperature over a month, it may seem like the temperature is changing slowly over the course of the month. However, if you plot the temperature over a day, it may seem like the temperature changes a lot more quickly.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# creating dataframe
df = pd.DataFrame({'x': range(1,11), 'y1': np.random.randn(10), 'y2': np.random.randn(10)+range(1,11), 'y3': np.random.randn(10)+range(11,21) })
# plotting line plot with multiple lines
plt.plot( 'x', 'y1', data=df, marker='o', markerfacecolor='blue', markersize=12, color='skyblue', linewidth=4)
plt.plot( 'x', 'y2', data=df, marker='', color='olive', linewidth=2)
plt.plot( 'x', 'y3', data=df, marker='', color='olive', linewidth=2, linestyle='dashed')
This code creates a line plot with multiple lines. The first line imports the matplotlib.pyplot library as plt. The second line imports the NumPy library as np. The third line imports the pandas library as pd.
The fourth line creates a dataframe called df. This dataframe has three columns: x, y1, and y2. The values in the x column range from 1 to 10. The values in the y1 column are random numbers generated by NumPy. The values in the y2 column are random numbers generated by NumPy plus the values in the x column (so they range from 2 to 11).
The fifth, sixth, and seventh lines plot the data in the df dataframe. The fifth line plots column y1 against column x using blue circles for markers. The sixth line plots column y2 against column x using no markers and olive-colored lines. The seventh line plots column y3 against column x using no markers, olive-colored lines, and dashed lines.
Plot
A plot is a sequence of events in a story. In Python, plots are represented by lists of tuples. Each tuple in the list corresponds to a particular event in the plot.
Seasonal Plot
A seasonal plot is a graphical representation of the time series data that shows the variation in the data over different periods. The plot can be used to identify any patterns in the data and to make predictions about future trends.
Plot tips
There are a few things to keep in mind when plotting data in Python. First, make sure that your data is in a format that can be plotted. Second, use the matplotlib library to plot your data. Third, use the pyplot module to control the plot and customize the appearance of your plots. Fourth, use the legend() function to add labels to your plots. Fifth, use the xlim() and ylim() functions to specify where on the plot your data should be displayed. Sixth, use the axvline() and axhline() functions to create lines and curves on your plots, respectively. Seventh, use the title() function to add a title to your plot and eighth, use the savefig() function to save your plot as a file
