Data Properties

In addition to the methods we just covered, DataFrames have a few useful properties for figuring out what's inside:

• The .shape property returns a tuple of the number of rows and columns in the DataFrame.
• The .columns property returns the names of the columns.
• The .dtypes property returns the data types of each column.

df = pd.read_csv("posts.csv")

print(df.shape)
# (997, 3)

print(df.columns)
# Index(['username', 'engagement_rate', 'platform'], dtype='object')

print(df.dtypes)
# username            object
# engagement_rate    float64
# platform            object

In the example above, we have:

• A DataFrame with 997 rows and 3 columns
• Columns named username (object), engagement_rate (float64), and platform (object)

You can get similar context by calling the .info() method, but these individual properties are convenient when you only need one piece of metadata.