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Pandas – Plot multiple time series DataFrame into a single plot

  • Difficulty Level : Medium
  • Last Updated : 17 Oct, 2021

In this article, we are going to see how to plot multiple time series Dataframe into single plot. 

If there are multiple time series in a single DataFrame, you can still use the plot() method to plot a line chart of all the time series. To Plot multiple time series into a single plot first of all we have to ensure that indexes of all the DataFrames are aligned. So let’s take two examples first in which indexes are aligned and one in which we have to align indexes of all the DataFrames before plotting.

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Plotting DataFrames with same DateTime Index:

Step 1: Importing Libraries

Python3






# importing Libraries
  
# import pandas as pd
import pandas as pd
  
# importing matplotlib module
import matplotlib.pyplot as plt
plt.style.use('default')
  
# %matplotlib inline: only draw static
# images in the notebook
%matplotlib inline

Step 2: Importing Data

We will be plotting open prices of three stocks Tesla, Ford, and general motors, You can download the data from here or yfinance library.

Tesla file:

Python3




# code
# importing Data
tesla = pd.read_csv('Tesla_Stock.csv',
                    index_col='Date'
                    parse_dates=True)
tesla.head(10)

Output:

Ford_stock:

Python3




# code
# importing data
ford = pd.read_csv('Ford_Stock.csv',
                   index_col='Date'
                   parse_dates=True)
ford.head(10)

Output:



GM_Stock:

Python3




# code
# importing data
gm = pd.read_csv('GM_Stock.csv',
                 index_col='Date',
                 parse_dates=True)
# printing 10 entries of the data
gm.head(10)

Output:

Step 3: Now Plotting Open Prices of the Stocks

Python3




# code
# Visualizing The Open Price of all the stocks
  
# to set the plot size
plt.figure(figsize=(16, 8), dpi=150)
  
# using plot method to plot open prices.
# in plot method we set the label and color of the curve.
tesla['Open'].plot(label='Tesla', color='orange')
gm['Open'].plot(label='GM')
ford['Open'].plot(label='Ford')
  
# adding title to the plot
plt.title('Open Price Plot')
  
# adding Label to the x-axis
plt.xlabel('Years')
  
# adding legend to the curve
plt.legend()

Output:

Plotting DataFrames with different DateTime Index:

In the second example, we will take stock price data of Apple (AAPL) and Microsoft (MSFT) off different periods. Our first task here will be to reindex any one of the dataFrame to align with the other dataFrame and then we can plot them in a single plot.

Step 1: Importing Libraries

Python3






# importing Libraries
  
# import pandas as pd
import pandas as pd
  
# importing matplotlib module
import matplotlib.pyplot as plt
plt.style.use('default')
  
# %matplotlib inline: only draw static images in the notebook
%matplotlib inline

Step 2: Importing Data

Python3




# code
aapl = pd.read_csv('aapl.csv',
                   index_col='Date',
                   parse_dates=True)
# printing 10 entries of the data
aapl.head(10)

Output:

msft file:

Python3




# importing Data
msft = pd.read_csv('msft.csv'
                   index_col='Date',
                   parse_dates=True)
# printing 10 entries of the data
msft.head(10)

Output:

As you can clearly see, DateTime index of both DataFrames is not the same, so firstly we have to align them. When we will make DateTime index of msft the same as that of all, then we will have some missing values for the period 2010-01-04 to 2012-01-02 , before plotting It is very important to remove missing values.

Python3




# Aligning index
aapl["MSFT"] = msft.MSFT
  
# removing Missing Values
aapl.dropna(inplace=True)
  
aapl.head(10)

Output:



We have merged the two DataFrames, into a single DataFrame, now we can simply plot it, 

Python3




# Visualizing The Price of the stocks
# to set the plot size
plt.figure(figsize=(16, 8), dpi=150)
  
# using .plot method to plot stock prices.
# we have passed colors as a list
aapl.plot(label='aapl', color=['orange', 'green'])
  
# adding title
plt.title('Price Plot')
  
# adding label to x-axis
plt.xlabel('Years')
  
# adding legend.
plt.legend()

Output:

In some cases we can’t afford to lose data, so we can also plot without removing missing values, plot for the same will look like:




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