Section 1.1 Memory Usage

Understanding memory consumption is crucial when working with large datasets. This method provides insights into the memory occupied by each column.

# Memory usage of a series

series = pd.Series(range(10))

series.memory_usage()

# Output:

208

Next, let's examine the memory usage of a DataFrame with multiple columns.

dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']

data = {dtype: np.ones(1000, dtype=int).astype(dtype) for dtype in dtypes}

df = pd.DataFrame(data)

df.memory_usage()

# Output:

Index 128

int64 8000

float64 8000

complex128 16000

object 8000

bool 1000

dtype: int64

Section 1.2 Copy Method

This method enables the duplication of data into another variable. The copy method includes a parameter for deep copying, which can be set to "true" or "false".

series = pd.Series([4.0, 6.0, 7.0, 12.0, 15.0], index=["a", "b", "c", "d", "e"])

# Default deep copy (deep=True)

series_copy = series.copy()

series_copy

# Output:

a 4.0

b 6.0

c 7.0

d 12.0

e 15.0

dtype: float64

# Shallow copy

shallow_copy = series.copy(deep=False)

Section 1.3 At Method

This method retrieves the value at a specified location within a DataFrame or Series.

df = pd.DataFrame(np.array([[4, 6, 9], [11, 14, 17]]),

index=['Apple', 'Kiwi'],

columns=['mm', 'cm', 'kg'])

df.at['Apple', 'cm']

# Output:

6

Section 1.4 Loc Method

This method allows access to values by specifying index positions.

df = pd.DataFrame(np.array([[4, 6, 9], [11, 14, 17]]),

index=['Apple', 'Kiwi'],

columns=['mm', 'cm', 'kg'])

df.loc['Kiwi']

# Output:

mm 11

cm 14

kg 17

Name: Kiwi, dtype: int32

# For columns

df.loc[df['cm'] > 6]

# Output:

mm cm kg

Kiwi 11 14 17

Additional Methods

To continue exploring various Pandas methods, check out the following video resources:

This video, "Basic Guide to Pandas! Tricks, Shortcuts, Must-Know Commands! Python for Beginners," provides a great introduction to these essential techniques.

More Advanced Techniques

For a deeper dive into advanced methods, view the next video:

"My Top 25 Pandas Tricks" showcases practical shortcuts for experienced users.