From b41799c3aaa4d872f36ec98cdcdea61b2190079f Mon Sep 17 00:00:00 2001
From: hnu202110040108 <napoleonandcaesar@163.com>
Date: Wed, 22 Jun 2022 14:15:20 +0800
Subject: [PATCH] ADD file via upload

---
 2022春pandas上课练习代码.py | 100 +++++++++++++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100644 2022春pandas上课练习代码.py

diff --git a/2022春pandas上课练习代码.py b/2022春pandas上课练习代码.py
new file mode 100644
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+++ b/2022春pandas上课练习代码.py
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+# -*- coding: utf-8 -*-
+"""
+Created on Mon May  2 14:20:17 2022
+
+@author: hzh
+"""
+import pandas as pd
+
+# Series 序列数据
+lst = [80, 80, 75, 60, 95]
+index = ["he", "wang", "liu", "zhang", "chen"]
+s1 = pd.Series(lst, index)
+print(s1.values)  # 返回的是一个numpy的数组
+print(s1.index)
+print(s1[0])
+print(s1['he'])
+
+dict1 = {"he": 80, "huang": 80, "liu": 75, "peng": 60, "yang": 95}
+s1 = pd.Series(dict1)
+print(s1)
+print(s1.values)  # 返回的是一个一维的numpy的数组
+print(s1.index)
+print(s1[0])
+print(s1['he'])
+
+# DataFrame数据
+data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
+        'year' : [2000, 2001, 2002, 2001, 2002],
+        'popu' : [1.5, 1.7, 3.6, 2.4, 2.9]}
+index = ['one', 'two', 'three', 'four', 'five']
+df = pd.DataFrame(data, index)
+print(df)
+print(df.columns)
+print(df.index)
+print(type(df.values))  # 返回的是一个二维的numpy.ndarray
+
+# DataFrame数据属性
+df.shape  # 行列数
+df.dtypes  # 列的数据类型
+df.ndim  # 数据维度
+df.head(3)
+df.tail(2)
+df.info()  # 相关信息概览
+df.describe()  # 快速综合统计结果
+
+# 索引1:[]列索引
+df.year
+df['year']
+df['state':'popu']  # 不能做列的切片索引
+df[['year', 'popu']]
+# 练习：输出第一列和最后一列
+df[['state', 'popu']]
+
+# 索引2:loc 行&列索引
+df.loc['one', 'year']
+df.loc['one':'three', 'year':'popu']
+df.loc[['one', 'five'], ['state', 'popu']]
+
+# 索引3 iloc 行号与列号索引
+df.iloc[0, 1]
+df.iloc[0:3, 1:2]
+
+# 索引4：布尔索引
+df.loc[df['year'] > 2000]
+df.loc[df['state'].str[0] == 'O']
+df[(df.year > 2001) & (df.state.str[0] == 'O')]
+df[(df.year > 2001) | (df.state.str[0] == 'O')]
+# 州名以O开头或者popu值大于2
+df[(df.popu > 2) | (df.state.str[0] == 'O')]
+
+# 增加
+dict2 = {'year': 2003, 'state': 'Louisiana', 'popu': 1.4}
+df1 = df.append(dict2, ignore_index=True)
+df1['debt'] = 1
+myprint(df1)
+
+# 删除
+df1.drop(5, inplace=True)
+myprint(df1)
+df1.drop('debt', axis=1, inplace=True)
+myprint(df1)
+
+# 修改
+df1['popu'] += 1
+myprint(df1, 'df1')
+
+# 合并
+df2 = df.copy()
+myprint(df2, 'df2')
+df3 = pd.concat([df2, df1], ignore_index=True)
+myprint(df3, 'df3')
+df4 = pd.concat([df2, df1], axis=1)
+myprint(df4)
+
+# 排序
+df5 = df.sort_values(by=['popu'], ascending=True)
+myprint(df5)
+df6 = df.sort_values(by=['year', 'popu'], ascending=False)
+myprint(df6)
+