Loading data into memory – viewing and managing with ease using pandas

First, we will need to load data into memory so that Python can interact with it. Pandas will be our data management and manipulation library:

# load data into Pandas
import pandas as pd
df = pd.read_csv("./data/iris.csv")

Let's use some built-in pandas features to do sanity checks on our data load and make sure that we've loaded everything properly. First, we use the .shape attribute to check the size of the data printed (as rows and columns). Next, we sanity check the contents of the DataFrame with the .head() method, which returns the first five lines in a new and smaller DataFrame for easy viewing. Finally, we can use the .describe() method to show some summary statistics for each feature. 

# sanity check with Pandas
print("shape of data in (rows, columns) is " + str(df.shape))
print(df.head())
print(df.describe().transpose())

You will see the following output after executing the preceding code: