Checking a DataFrame’s Attributes

What are the key attributes of a DataFrame?

Question: What are the key attributes of a DataFrame?

Answer: Pandas DataFrame can be characterized along several attributes, such as:

Shape
Size
Number of dimensions
Set of column names
Set of case indices

A convenient way to access multiple DataFrame’s attributes in a row is by using the .info method. Per the code snippet below (see lines 18-26), we know that df has three entries and as many indices, two columns (an object, country, and a float gdp_pc), and occupies circa 176 bytes in memory.

The bottom section of the code snippet shows how to access the individual attributes of a DataFrame using .shape, .size, .ndim, and .memory_usage.

# import pandas with the socially accepted alias 'pd'
>>> import pandas as pd

# create a DataFrame from a dictionary
>>> gdp_data = {
    "country": ["Belgium", "France", "Germany"],
    "gdp_pc": [51767.8, 43518.5, 50801.8]
    }
>>> df = pd.DataFrame.from_dict(gdp_data)
>>> df
   country   gdp_pc
0  Belgium  51767.8
1   France  43518.5
2  Germany  50801.8

# get DataFrame 'info'
>>> df.info()
RangeIndex: 3 entries, 0 to 2
Data columns (total 2 columns):
     Column   Non-Null Count  Dtype
---  ------   --------------  -----
 0   country  3 non-null      str
 1   gdp_pc   3 non-null      float64
dtypes: float64(1), str(1)
memory usage: 180.0 bytes

# let us get df's attributes one-by-one
# --+ shape (cases by columns)
>>> df.shape
(3, 2)
# --+ size (cases X columns)
>>> df.size
6
# --+ number of dimensions (columns)
>>> df.ndim
2
# --+ check memory usage column-by-column
>>> df.memory_usage()
Index      132
country     24
gdp_pc      24
dtype: int64