Description

get particular row from dataframe ?

# How To Retrieve a Particular Row from a DataFrame in Pandas

In data analysis and manipulation tasks, you often require fetching specific rows from a DataFrame based on certain conditions or indices. In the world of Python programming, especially with the powerful Pandas library, this process is quite straightforward. This post will guide you through different methods to obtain a specific row in a DataFrame using multiple indexing techniques – `iloc[]`, `loc[]`, and Boolean indexing.

## Understanding Pandas DataFrame

Before jumping into selecting rows, it’s essential to understand what a DataFrame is. Simply put, a DataFrame is a two-dimensional, size-mutable data structure with labeled axes (rows and columns), which is great for data analysis tasks. In Pandas, DataFrames are used extensively.

## The Basics: Selecting a Row Using `iloc[]`

The `iloc[]` is essential for selecting rows by integer location. The `iloc[]` index corresponds to the row number. Here’s a brief syntax and example:

**Syntax:**

“`python
pandas.DataFrame.iloc[]
“`

**Parameters:**

– `Index Position`: Accepts the row index (int) or list of row indices.

**Return Type:**

– Returns a Series or DataFrame depending on the argument passed.

### Example:

“`python
import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
‘Product’: [‘Laptop’, ‘Tablet’, ‘Smartphone’, ‘Monitor’],
‘Price’: [1200, 400, 800, 300],
‘Units’: [3, 10, 5, 8]
})

# Get the second row (index 1)
print(df.iloc[1])
“`

**Output:**
“`shell
Product Tablet
Price 400
Units 10
Name: 1, dtype: object
“`

If you convert the row to a DataFrame, you can use double brackets `iloc[[int]]`.

“`python
print(df.iloc[[1]])
“`

**Output:**
“`
Product Price Units
1 Tablet 400 10
“`

## Selecting Rows Using `loc[]`

The `loc[]` function helps in selecting rows based on the label index and also with a Boolean array. This is particularly handy when you have a DataFrame with non-integer labels.

### Example:

“`python
# Set the index to Product for the DataFrame
df.set_index(‘Product’, inplace=True)

# Get the row using label
print(df.loc[‘Smartphone’])
“`

**Output:**
“`shell
Price 800
Units 5
Name: Smartphone, dtype: int64
“`

## Boolean Indexing to Filter Rows

Boolean indexing is another powerful feature that arises when you want to select rows that match a condition. This is useful in data filtering.

### Example:

“`python
# Get the rows where Units > 5
print(df[df[‘Units’] > 5])
“`

**Output:**
“`shell
Price Units
Product
Laptop 1200 3
Tablet 400 10
Monitor 300 8
“`

## Turning Rows into Series

Often, you may want to get a particular row as a Series. This is quite simple using `iloc`.

### Example:

“`python
# Get the first row as a Series
row_series = df.iloc[0]
print(row_series)
“`

**Output:**
“`shell
Price 1200
Units 3
Name: Laptop, dtype: int64
“`

## Conclusion

Selecting rows in a DataFrame can be done in multiple ways, depending on your specific needs. Using `iloc[]`, `loc[]`, and Boolean indexing provides great flexibility in data manipulation. Whether you are working with large datasets or small ones, mastering these techniques will save you a lot of time and make your data analysis process more efficient.

Remember the following:

– Use `iloc[]` for selecting rows by integer location.
– Use `loc[]` for rows based on labels.
– Use Boolean indexing for filtering data based on a condition.

Feel free to try these methods with your own data to get a feel for how it works. Happy coding!

## Resources

– [GeeksforGeeks on Getting Specific Rows](https://www.geeksforgeeks.org/get-a-specific-row-in-a-given-pandas-dataframe/)
– [Data to Fish on Selecting Rows](https://datatofish.com/select-rows-pandas-dataframe/)
– [Statology on Selecting Rows by Index](https://www.statology.org/pandas-select-rows-by-index/)
– [StackOverflow on Selecting Rows](https://stackoverflow.com/questions/67324351/how-to-get-value-from-a-row-in-pandas-dataframe)
– [How To Get a Particular Row as Series](https://www.includehelp.com/python/get-particular-row-as-series-from-pandas-dataframe.aspx)

Explore these links for more detailed examples and additional functionalities in Pandas.