How to Generate Line Plot in a DataFrame?

Pandas DataFrame.plot() method is used to generate a line plot from the DataFrame. A line plot is the default plot. It Provides the plotting of one column to another column. If not specified, by default plotting is done over the index of the DataFrame to another numeric column.

In this article, I will explain the concept of a line plot and using plot() how to plot the line from the given Pandas DataFrame.

1. Quick Examples of Line Plot

Following are quick examples of how to create a line plot.


# Below are quick examples 

# Example 1: Create a line plot 
seattle_temps['temp'].plot()

# Example 2: Default line plot
df.plot()

# Example 3: Get the single line plot
df['min'].plot()

# Example 4: Customize the Line plot of DataFrame
df.plot(rot = 60)
plt.xlabel("Index", size = 20)
plt.ylabel("Temp", size = 20)
plt.title("Minimum temperature of Seattle", size = 25)

# Example 5: Create multiple line on separate plots
df.plot(subplots = True)

# Example 6: Create timeseries plot
df.plot(x="date", y="min")
plt.xlabel("Date",  size = 20)
plt.ylabel("Minimum Temperature", size = 20)
plt.title("Minimum temperature of Seattle", size = 25)

2. Syntax of Pandas plot()

Following is the syntax of the plot() function which I will be using to create a time series plot.


# Syntax of plot()
DataFrame.plot(*args, **kwargs)

2.1 Parameters of plot() function

Following are the parameters of the plot() function.

  • data: Series or DataFrame.
  • x: label or position, default None. Only used if data is a DataFrame.
  • y: label, position or list of label, positions, default None. It allows the plotting of multiple columns. Only used if data is a DataFrame.
  • Kind: It defines the type of plot to be created, default value is line.

The kind of plot to produce:

  • line - line plot (default)
  • bar - vertical bar plot
  • barh - horizontal bar plot
  • hist - histogram
  • box - boxplot
  • kde - Kernel Density Estimation plot
  • density - same as ‘kde’
  • area - area plot
  • pie - pie plot
  • scatter - scatter plot (DataFrame only)
  • hexbin - hexbin plot (DataFrame only)
  • **kwargs: Options to pass to matplotlib plotting method.

2.2 Return Value

It returns matplotlib.axes.Axes or numpy.ndarray of them

3. Introduction of Plot.

Python Pandas library is mainly focused on data analysis and it is not only a data visualization library but also using this we can create basic plots. When we want to develop exploratory data analysis plots, pandas is highly useful and practical. It provides plot() and several other wrapper functions for visualizing our data.

Let’s use this pandas plot() function to create a time series plot. Here I have taken weather data of Seattle city from vega_datasets and using pandas I will plot the line plot of the given dataset.

To access these datasets from Python, you can use the Vega datasets python package. Let’s import weather data of Seattle city, Here columns are date and temp. The date column is in the form of yyyy-mm-dd.


# Import weather dataset
import pandas as pd
import numpy as np
from vega_datasets import data
import matplotlib.pyplot as plt

# Load seattle temperature data
seattle_temps = data.seattle_temps()
print(seattle_temps.shape)
print(seattle_temps.head())
print(seattle_temps.tail())

Yields below output.


# shape() output
(8759, 2)

# head() output
                 date  temp
0 2010-01-01 00:00:00  39.4
1 2010-01-01 01:00:00  39.2
2 2010-01-01 02:00:00  39.0
3 2010-01-01 03:00:00  38.9
4 2010-01-01 04:00:00  38.8

# tail() output
                    date  temp
8754 2010-12-31 19:00:00  40.7
8755 2010-12-31 20:00:00  40.5
8756 2010-12-31 21:00:00  40.2
8757 2010-12-31 22:00:00  40.0
8758 2010-12-31 23:00:00  39.6

4. Pandas plot() Function to Create Sample line Plot

We can directly pass temp column into plot() function to create a line plot by using the above specific column of Seattle’s weather data.


# Create a line plot 
seattle_temps['temp'].plot()

Yields below output.

Pandas line plot
Line plot of temperature

As you can see from the above, we have got a line plot with all the data, here band showing the minimum and maximum temperature for every data. For every hour the temperature data changes over a day. Also, we can observe indices of DataFrame on the x-axis, not the date column.

4.1 Extract Date from Datetime

Let’s remove the time part from datetime column.


# Convert date column as simple date
seattle_temps['date'] = seattle_temps['date'].dt.date
print(seattle_temps.tail())

Yields below output.


# Output:
            date  temp
8754  2010-12-31  40.7
8755  2010-12-31  40.5
8756  2010-12-31  40.2
8757  2010-12-31  40.0
8758  2010-12-31  39.6

Let’s also get minimum and maximum temperatures for each day using Pandas groupby() function along with pandas agg() function.


# Get the min & max temparatures
df = seattle_temps.groupby('date').agg(['min','max'])
print(df)

Yields below output.


# Output:
           temp      
             min   max
date                  
2010-01-01  38.6  43.5
2010-01-02  38.8  43.8
2010-01-03  39.0  44.0
2010-01-04  39.2  44.2
2010-01-05  39.3  44.4
         ...   ...
2010-12-27  37.9  42.8
2010-12-28  38.1  43.0
2010-12-29  38.1  43.0
2010-12-30  38.2  43.1
2010-12-31  38.4  43.3

[365 rows x 2 columns]

Using the pd.droplevel() function we can drop the multi-level column index, here I can drop the level 0 index of a given DataFrame to make a flattened Dataframe. Then, reset the index using reset_index() function.


# drop the level 0 column & set the index
df.columns = df.columns.droplevel(0)
df.reset_index(level=0, inplace=True)
print(df.head())

Yields below output.


# Output:
         date   min   max
0  2010-01-01  38.6  43.5
1  2010-01-02  38.8  43.8
2  2010-01-03  39.0  44.0
3  2010-01-04  39.2  44.2
4  2010-01-05  39.3  44.4

5. Default Line Plot using DataFrame

Here, I will create a line plot of the given DataFrame using plot() function, it will take default indices on the x-axis and min and max columns on the y-axis. Finally, it will return the double-line plot.


# Default line plot
df.plot()

Yields below output.

Pandas line plot
Line plot of Pandas DataFrame

6. Make a Single Line plot

By using the above-created dataframe let’s plot the min temperature across different days.


# Get the single line plot
df['min'].plot()

Yields below output.

pandas line plot
Minimum temperature of Line Plot with Pandas

7. Customize the Line Plots

We can customize the plots using any keyword arguments pass into plot() function. rot keyword allows rotating the markings on the x-axis for horizontal plotting and y-axis for vertical plotting, size keyword allows to set the font size for the labels of axis points and title of the plots, and colormap keyword argument allows to choose different color sets for the plots.

Using Matplotlib.pyplot we can give the labels of the axes and the title of the plot. For example, Here, I use the rot keyword into plot() function, it will rotate the marking of the x-axis horizontally.


# Customize the Line plot of DataFrame
df.plot(rot = 60)
plt.xlabel("Index", size = 20)
plt.ylabel("Temp", size = 20)
plt.title("Minimum temperature of Seattle", size = 25)
pandas line plot
Line Plot of temperature using Pandas

8. Plot Multiple Lines on Separate Plots

We can create multiple lines on separate plots using plot() function. For that, we will set and pass the keyword argument argument subplots = True into this function, it will create multiple lines on separate plots.


# Create multiple line on separate plots
df.plot(subplots = True)
Pandas line plot
Multiple line plots

9. Create Timeseries plot in Pandas

Let’s create timeseries plot with minimum temperature on y-axis and date on x-axis using plot() function directly on the DataFrame.


# Create timeseries plot
df.plot(x="date", y="min")
plt.xlabel("Date",  size = 20)
plt.ylabel("Minimum Temperature", size = 20)
plt.title("Minimum temperature of Seattle", size = 25)
Pandas line plt=ot
Minimum temperature of timeseries Plot with Pandas

10. Conclusion

In this article, I have explained the concept of line plot and by using the plot() function how to plot the line plot or time series of DataFrame. Also explained how we can customize the line plot and timeseries using optional parameters.

Happy learning !!

References

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