2020 Python data analysis study notes for Pandas data filtering, conditional query and addition, deletion, modification and query (7)

table of Contents

 

1. Data screening

2. Explanation of loc and iloc functions

3. Conditional query and addition, deletion, modification and query

 

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1. Data screening

Practice data download:

https://download.csdn.net/download/weixin_44940488/12660653

Code display:

(Basic syntax for data filtering)

import pandas as pd
import numpy as np

data = pd.read_excel('score.xlsx', encoding='utf-8',sheet_name= 0)     # 读取数据    sheet_name= 0  选择第一个工作薄

print(data)       #  输出全部数据

print(data.head(5))        # 输出前五行数据

print(data.tail(5))         # 输出后五行数据

print(data.columns)        # 输出列名

print(data.dtypes)         # 输出变量类型

print(data.ndim)           # 输出数据结构

print(data.shape)           # 输出数据形状

print(data.size)            # 输出数据大小，总共有多少个元素

print(data[:10])             # 查看数据前十行数据

# 方法一
# print(data.代码)            # 读取数据中某一指定列的数据
# 方法二
print(data['代码'][:6])      # 读取代码的前六列数据
# print(data[['通过个数','代码']])  # 读取指定多列数据

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2. Explanation of loc and iloc functions

Code display:

import pandas as pd
import numpy as np

data = pd.read_excel('score.xlsx', encoding='utf-8',sheet_name= 0)     # 读取数据    sheet_name= 0  选择第一个工作薄

print(data.head(10))

# loc函数讲解：

print(data.loc[0:6])     # 读取前7行数据

print(data.loc[4,['题目ID','代码']])        # 读取行标签为4的数据

print(data.loc[[4,7],['题目ID','代码']])    # 读取行标签为4和7的数据

print(data.loc[data['题目ID'] == 888, ['题目ID','代码']])    # 读取题目ID为888的数据

# iloc函数讲解
print(data.iloc[:,1:4])     # 读取数据2，3，4列数据

print(data.iloc[0:6])       # 读取前6行数据

Result display:

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3. Conditional query and addition, deletion, modification and query