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Python | Pandas Extracting rows using .loc[]

The .loc[] accessor in pandas is a versatile tool to select rows (and columns) from a DataFrame based on labels. Here's a tutorial on how to extract rows using .loc[]:

1. Setup:

First, ensure you have pandas installed:

pip install pandas

2. Import Necessary Libraries:

import pandas as pd

3. Create a Sample DataFrame:

data = {
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12]
}
df = pd.DataFrame(data, index=['w', 'x', 'y', 'z'])
print(df)

This will produce:

   A  B   C
w  1  5   9
x  2  6  10
y  3  7  11
z  4  8  12

4. Extract Rows Using `.loc[]`:

a. Single Row by Index Label:

print(df.loc['x'])

b. Multiple Rows by Index Labels:

print(df.loc[['w', 'y']])

c. Range of Rows by Index Labels:

print(df.loc['w':'y'])

5. Extract Rows and Specific Columns:

You can also specify which columns you want:

a. Single Row, Single Column:

print(df.loc['x', 'A'])

b. Multiple Rows, Single Column:

print(df.loc[['w', 'y'], 'B'])

c. Multiple Rows, Multiple Columns:

print(df.loc[['w', 'y'], ['A', 'C']])

6. Using Conditions:

You can also use conditions to extract rows based on certain criteria:

print(df.loc[df['A'] > 2])

This will retrieve all rows where the value in column 'A' is greater than 2.

7. Important Notes:

.loc[] primarily works with labels. Ensure that the indices or columns you specify exist in the DataFrame, or you'll encounter a KeyError.
When slicing with .loc[], both the start and the end points are inclusive, which is different from Python's standard list slicing.

Summary:

The .loc[] accessor in pandas is a powerful tool to select specific rows and columns from a DataFrame based on labels. It allows for a wide range of flexible operations, from simple row selection to more complex conditional extraction. Familiarizing yourself with .loc[] is crucial for efficient data manipulation in pandas.

Extract rows with .loc in Pandas DataFrame:
- Use .loc for label-based row selection.
```
selected_rows = df.loc[2:5]
```
How to use .loc for row selection in Pandas:
- Utilize .loc for selecting specific rows.
```
selected_rows = df.loc[[1, 3, 5]]
```
Pandas .loc for label-based indexing:
- Apply .loc for label-based indexing of rows.
```
selected_row = df.loc[2]
```
Selecting specific rows using .loc in Pandas:
- Choose specific rows using label-based indexing with .loc.
```
specific_rows = df.loc[[2, 4, 6]]
```
Conditional row extraction with .loc in Pandas:
- Use .loc with conditions for extracting rows.
```
condition_rows = df.loc[df['column'] > 50]
```
Extracting rows by index label in Pandas:
- Extract rows using index labels with .loc.
```
labeled_rows = df.loc[df.index.isin([1, 3, 5])]
```
Advanced row selection with .loc in Pandas:
- Perform advanced row selection with .loc.
```
advanced_selection = df.loc[df['column1'] > 50, ['column2', 'column3']]
```
Pandas .loc vs .iloc for row extraction:
- Understand the difference between .loc and .iloc for row extraction.
```
loc_selection = df.loc[2:5, 'column']
iloc_selection = df.iloc[2:5, 1]
```
Using .loc to extract rows based on conditions in Pandas:
- Extract rows based on conditions using .loc.
```
condition_rows = df.loc[(df['column1'] > 50) & (df['column2'] == 'value')]
```