From d7094bb0c6ef0673c27553deb66dc3405e2a9aa1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=89=8D=E7=AB=AF=E8=8F=9C13?=
 <38740836+fyhhub@users.noreply.github.com>
Date: Thu, 7 Nov 2024 21:04:27 +0800
Subject: [PATCH] feat: changes

---
 .../1.python\345\237\272\347\241\200.md"      | 83 ++++++++++++++++++-
 1 file changed, 82 insertions(+), 1 deletion(-)

diff --git "a/src/python/1.python\345\237\272\347\241\200.md" "b/src/python/1.python\345\237\272\347\241\200.md"
index c016c0b78..c94f19a60 100644
--- "a/src/python/1.python\345\237\272\347\241\200.md"
+++ "b/src/python/1.python\345\237\272\347\241\200.md"
@@ -1,3 +1,84 @@
-# Python基础
+# Pandas使用
 
+```py
+pip install pandas
+```
 
+
+## 数据读取
+
+### 1. 读取表格
+```py
+# -*- coding: utf-8 -*-
+df = pd.read_csv(encoding='utf-8', filepath_or_buffer='xxx')
+
+
+df = pd.read_excel('example.xlsx', usecols=['A', 'B'])
+```
+
+### 2.获取表头
+```py
+df.columns
+```
+
+### 3.获取某一列去重后的值
+```py
+df['column_name'].unique()
+```
+
+## 数据操作
+
+### 1.填充空数据
+```py
+df['pageUrl'].fillna('')
+```
+
+
+### 2.修改某列的所有数据
+```py
+df['pageUrl'] = df['pageUrl'].map(lambda x: 'xxxx' + str(x))
+```
+
+### 3.新建表格
+```py
+data = {
+  'name': ['Alice', 'Bob', 'Charlie', 'David'],
+  'age': [23, 30, 45, 25],
+  'salary': [70000, 80000, 120000, 95000]
+}
+
+# 创建 DataFrame
+df = pd.DataFrame(data)
+```
+
+
+
+## 数据筛选
+
+### 1.筛选缺失的值
+```py
+df['年龄'].isna()
+```
+
+### 2.筛选出不包含特定值的行
+```py
+df[~df['姓名'].isin(['张三'])]
+```
+
+### 3.筛选日期
+```py
+import datetime
+
+# 将 'timestamp' 列转换为日期时间格式
+df['timestamp'] = df['timestamp'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))
+
+# 提取 '年月日' 并去重
+unique_days = df['timestamp'].dt.date.unique()
+```
+
+## 数据计算
+
+### 1.根据某种条件，对某一列求和
+```py
+df[df['关联需求类型'] == '业务需求']['实际工时'].sum()
+```
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