Print Rows

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1.Selecting Single Rows with iloc[Original Blog]

In the world of data analysis, selecting and manipulating subsets of data is an essential task. This is where iloc comes in handy. Iloc is a method in pandas that allows us to select specific rows and columns from a DataFrame by their integer index positions. In this blog, we will focus on selecting single rows with iloc and how it simplifies the subset selection process.

1. Basics of iloc:

Iloc is a shorthand for "integer location" and is used to select rows and columns by their integer position. It is a powerful tool that allows us to extract specific data from a DataFrame. Here is an example of how to select a single row using iloc:

```python

Import pandas as pd

Df = pd.read_csv('data.csv')

# select the first row

Row = df.iloc[0]

Print(row)

```

This will print the first row of the DataFrame. We can also select multiple rows by passing a list of integers to iloc.

2. Selecting a range of rows:

In addition to selecting a single row, we can also select a range of rows using iloc. Here is an example:

```python

Import pandas as pd

Df = pd.read_csv('data.csv')

# select the first three rows

Rows = df.iloc[0:3]

Print(rows)

```

This will print the first three rows of the DataFrame. Note that iloc uses a zero-based index, so the first row is at index position 0.

3. Selecting rows based on a condition:

We can also use iloc to select rows based on a condition. For example, let's say we want to select all the rows where the value in the "age" column is greater than 30. Here is how we can do it:

```python

Import pandas as pd

Df = pd.read_csv('data.csv')

# select rows where age is greater than 30

Rows = df.iloc[df['age'] > 30]

Print(rows)

```

This will print all the rows where the value in the "age" column is greater than 30.

4. Comparison with loc:

Iloc is similar to loc, which is another method in pandas used for selecting subsets of data. However, loc uses labels instead of integer positions. While both methods can achieve the same result, iloc is generally faster when working with large datasets. It is also more intuitive when selecting rows based on integer positions.

Iloc is a powerful tool for selecting specific rows from a DataFrame based on their integer positions. It simplifies the subset selection process and is faster than other methods when working with large datasets. By understanding how to use iloc, we can make our data analysis tasks more efficient and effective.

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