How Matplotlib and Pandas are used? The Basics

In this tutorial, we will be working with streamflow data and using the pandas and matplotlib libraries to analyze and visualize the data.

Streamflow Data

We will start by generating random streamflow data using the numpy library. We will create an array of 365 random numbers between 5 and 15, representing the streamflow for each day of the year.

num_days = 365
streamflow = np.random.uniform(low=5,high=15,size=num_days)

Creating a Date Range

Next, we will create a date range for the data using the pandas library. We will create a date range from January 26, 2000 to December 31, 2000, with 365 days.

dates = pd.date_range(start='2000-01-26', periods=num_days)

Creating a DataFrame

We will then create a DataFrame using the pandas library. The DataFrame will have two columns: ‘Date’ and ‘flow’. The ‘Date’ column will contain the date range we created earlier, and the ‘flow’ column will contain the streamflow data.

df = pd.DataFrame({'Date':dates,'flow':streamflow})

Printing the DataFrame

We can print the first few rows of the DataFrame using the head() function.

print(df.head())

Basic Statistics

We can calculate basic statistics of the streamflow data using the describe() function.

print(df['flow'].describe())

Plotting the Data

Finally, we will plot the streamflow data using the matplotlib library. We will create a line plot with the date on the x-axis and the streamflow on the y-axis.

plt.figure(figsize=(12,8))
plt.plot(df['Date'],df['flow'],marker='o',linestyle ='-',color='grey',label='Streamflow')
plt.title('Daily Streamflow')
plt.xlabel('Date')
plt.ylabel('flow m3/s')
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()

Understanding

Pandas provides data structures and functions to efficiently handle structured data, including tabular data such as spreadsheets and SQL tables.

Matplotlib is a plotting library for Python. It provides a wide range of tools for creating high-quality 2D and 3D plots, charts, and graphs. It is often used in conjunction with pandas to visualize data.

Exploring Matplotlib in detail

  1. plt.figure(figsize=(12,8)):

    • This function creates a new figure for the plot.
    • The figsize parameter sets the size of the figure to 12 inches wide and 8 inches tall.

  2. plt.plot(df['Date'],df['flow'],marker='o',linestyle ='-',color='grey',label='Streamflow'):

    • This function creates a line plot.
    • The first argument, df['Date'], specifies the x-axis data (dates).
    • The second argument, df['flow'], specifies the y-axis data (streamflow).
    • The marker='o' parameter sets the marker style to a circle for each data point.
    • The linestyle ='-' parameter sets the line style to a solid line.
    • The color='grey' parameter sets the color of the line and markers to grey.
    • The label='Streamflow' parameter sets the label for the line, which will be used in the legend.

  3. plt.title('Daily Streamflow'):

    • This function sets the title of the plot to “Daily Streamflow”.

  4. plt.xlabel('Date'):

    • This function sets the label for the x-axis to “Date”.

  5. plt.ylabel('flow m3/s'):

    • This function sets the label for the y-axis to “flow m3/s”.

  6. plt.grid(True):

    • This function adds a grid to the plot.
    • The True parameter turns on the grid.

  7. plt.legend():

    • This function adds a legend to the plot.
    • The legend shows the label for each line in the plot.

  8. plt.tight_layout():

    • This function adjusts the spacing between the subplots and the figure edge to make the plot look neat and tidy.

  9. plt.show():

    • This function displays the plot.

Script

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

#Streamflow data
#Lets generate random streamflow data
num_days = 365
#streamflow = np.random.randint(low=5,high=15,size=num_days)
streamflow = np.random.uniform(low=5,high=15,size=num_days)

#print(streamflow)

#create date range for data
dates =pd.date_range(start='2000-01-26',periods=num_days)
#print(dates)

#create dataframa
df = pd.DataFrame({'Date':dates,'flow':streamflow})
#print(type(streamflow),type(dates),type(df))
#print(df)

#to print first few data, we can use head function 
print(df.head())

#Basic Statistics of streamflow data
print(df['flow'].describe())

#Lets plot the data now
plt.figure(figsize=(12,8))
plt.plot(df['Date'],df['flow'],marker='o',linestyle ='-',color='grey',label='Streamflow')
plt.title('Daily Streamflow')
plt.xlabel('Date')
plt.ylabel('flow m3/s')
plt.grid(True)
plt.legend()
plt.tight_layout()
plt.show()