Before anything else, let’s import the necessary libraries: pandas (for reading in CSV files), numpy (for doing math stuff), matplotlib (for making pretty graphs), and seaborn (for making even prettier graphs). We also need to set up a function that will read in our data from a file called “data.csv” located in the same directory as this script:
# Import necessary libraries
import pandas as pd # Import pandas library and alias it as 'pd'
import numpy as np # Import numpy library and alias it as 'np'
import matplotlib.pyplot as plt # Import matplotlib library and alias it as 'plt'
import seaborn as sns # Import seaborn library and alias it as 'sns'
# Define a function to load data from a CSV file
def load_data():
# Read in data from CSV file and store it in a DataFrame called 'df'
df = pd.read_csv('data.csv') # Use the read_csv function from pandas to read in the data from the 'data.csv' file and store it in a DataFrame called 'df'
return df # Return the DataFrame 'df' to be used outside of the function
# Call the load_data function to read in the data from the CSV file and store it in a DataFrame called 'df'
df = load_data()
Now that we have our function set up, let’s call it to load the data:
# Load data using the 'load_data()' function and store it in a variable called 'df'
# The function 'load_data()' is called to load the data and the result is stored in a variable called 'df'
df = load_data()
Once we have our data loaded into a DataFrame called `df`, we can start doing some analysis. Let’s say we want to find out what the average value of column “A” is:
# Import the numpy library to use its functions
import numpy as np
# Calculate the mean (average) value for column 'A' using numpy's 'mean()' function and store it in a variable called 'avg_a'
avg_a = np.mean(df['A']) # Use the mean() function from numpy to calculate the average value of column 'A' in the DataFrame 'df' and store it in the variable 'avg_a'
# Print the result
print("The average value of column A is: ", avg_a) # Print the average value of column 'A' using the variable 'avg_a'
Or maybe we want to see how many values are greater than 10 for column “B”:
# Count the number of rows (i.e., observations) where column 'B' is greater than 10 using pandas' 'query()' function and store it in a variable called 'count_b'
count_b = len(df.query('B > 10')) # Using the 'query()' function from pandas, we can filter the dataframe 'df' to only include rows where column 'B' is greater than 10. The 'len()' function is then used to count the number of rows in the filtered dataframe and store it in the variable 'count_b'.
# Print the result
print("The number of values for column B that are greater than 10 is: ", count_b) # The result is printed to the console, displaying the number of rows where column 'B' is greater than 10.
And if we want to see how the data looks, we can create a scatter plot using matplotlib and seaborn:
# Import necessary libraries
import matplotlib.pyplot as plt # Importing matplotlib library for creating plots
import seaborn as sns # Importing seaborn library for data visualization
# Create a scatter plot with x-axis variable 'A' and y-axis variable 'B', using seaborn's 'scatterplot()' function
sns.scatterplot(x=df['A'], y=df['B']) # Using the scatterplot() function from seaborn to create a scatter plot with x-axis as column 'A' and y-axis as column 'B' from the dataframe 'df'
plt.show() # Displaying the plot on the screen
That’s just a small taste of what you can do with this script! The possibilities are endless, and the best part is that it’s all done in Python one of the easiest programming languages to learn. So go ahead, give it a try, and let us know how it goes!