How to use the iloc function in Python

The iloc function in Python is used to select data by integer position. It can be used on DataFrame and Series objects in the pandas library.

In a DataFrame, the iloc function can be used to select data based on the integer position of rows and columns. It uses a zero-based index where 0 represents the first row/column, 1 represents the second row/column, and so on. The basic syntax of the iloc function is as follows:

df.iloc[row_index, column_index]

In this case, row_index is used to specify the integer position or slice object of the row, and column_index is used to specify the integer position or slice object of the column. Specific rows or columns can be selected by passing a single integer or integer list.

For example, consider the following DataFrame object:

import pandas as pd

data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10],
        'C': [11, 12, 13, 14, 15]}
df = pd.DataFrame(data)

You can use the iloc function to select data from the first row and second column.

print(df.iloc[0, 1])  # 输出：6

You can also use the iloc function to select data from multiple rows and columns.

print(df.iloc[0:3, 1:3])  # 输出：
#    B   C
# 0  6  11
# 1  7  12
# 2  8  13

The usage of the iloc function in the Series object is similar to that of the DataFrame object, focusing only on selecting rows. It can select data based on the integer position of rows or a slice object.

For example, consider the following Series object:

import pandas as pd

data = pd.Series([1, 2, 3, 4, 5])

You can use the iloc function to select the second element.

print(data.iloc[1])  # 输出：2

You can also use the iloc function to select multiple elements:

print(data.iloc[1:4])  # 输出：
# 1    2
# 2    3
# 3    4
# dtype: int64

This is how the iloc function is used in Python. It is very useful for selecting data based on integer position.