pandas数据分析练习记录
【摘要】
pandas数据分析练习
# coding=utf-8""" @Project ：pachong-master @File ：list_series.py @Author ：gaojs @Date ：2022/6/5 22:06 @Blogs : https://www.gao...
pandas数据分析练习
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# coding=utf-8
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"""
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@Project ：pachong-master
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@File ：list_series.py
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@Author ：gaojs
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@Date ：2022/6/5 22:06
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@Blogs : https://www.gaojs.com.cn
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"""
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import pandas as pd
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import numpy as np
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class Pandas:
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"""
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pandas类库练习
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"""
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def __init__(self):
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pass
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def list_series(self):
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"""
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列表转换成series:索引为数字
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:return:
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"""
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course = ["语文", "数学", "英语", "计算机"]
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data = pd.Series(data=course)
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print(data)
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def dict_series(self):
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"""
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字典转换成series：索引为key值
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:return:
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"""
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grades = {"语文": 80, "数学": 90, "英语": 100, "计算机": 95}
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data = pd.Series(data=grades)
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print(data)
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def series_list(self):
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"""
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series转换成list
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:return:
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"""
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grades = {"语文": 80, "数学": 90, "英语": 100, "计算机": 95}
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data = pd.Series(data=grades)
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members = data.to_list()
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print(members)
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def series_dataframe(self):
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"""
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series转换成dataframe
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:return:
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"""
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grades = {"语文": 80, "数学": 90, "英语": 100, "计算机": 95}
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data = pd.Series(data=grades)
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df = pd.DataFrame(data, columns=['grade'])
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print(df)
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def numpy_create_series(self):
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"""
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numpy创建series
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:return:
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"""
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s = pd.Series(
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# 树脂：10-90，间隔10
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np.arange(10, 100, 10),
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# 索引：101-109，间隔1
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index=np.arange(101, 110),
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# 类型：float64
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dtype='float'
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)
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print(s)
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def series_datatype(self):
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"""
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数据类型转换
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:return:
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"""
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s = pd.Series(
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data=["001", "002", "003", "004"],
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index=list("abcd")
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)
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# s = s.astype(int)
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# 效果一样,map里的int是函数
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s = s.map(int)
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print(s)
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def series_add_ele(self):
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"""
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给series添加新元素
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:return:
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"""
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grades = {"语文": 80, "数学": 90, "英语": 100, "计算机": 95}
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data = pd.Series(data=grades)
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data = data.append(pd.Series({
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"物理": 99,
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"高数": 88
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}))
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print(data)
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def series_to_dataframe(self):
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"""
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series转换成dataframe
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:return:
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"""
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grades = {"语文": 80, "数学": 90, "英语": 100, "计算机": 95}
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data = pd.Series(data=grades)
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df = data.reset_index()
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df.columns = ['course', 'grade']
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print(df)
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def dict_create_dataframe(self):
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"""
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使用字典创建dataframe
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:return:
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"""
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df = pd.DataFrame(
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{
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"姓名": ["张三", "李四", "王麻子", "高先生"],
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"性别": ["男", "女", "男", "女"],
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"年龄": [18, 19, 20, 21]
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}
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)
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print(df)
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def set_dataframe_index(self):
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"""
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给dataframe设置索引列
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:return:
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"""
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df = pd.DataFrame(
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{
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"姓名": ["张三", "李四", "王麻子", "高先生"],
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"性别": ["男", "女", "男", "女"],
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"年龄": [18, 19, 20, 21]
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}
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)
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df.set_index("姓名", inplace=True)
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print(df)
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def dataMonth(self):
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"""
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一个月份所有日期
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:return:
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"""
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# date_range = pd.date_range(start='2022-05-01', end='2022-05-31')
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date_range = pd.date_range(start='2022-05-01', periods=31)
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print(date_range)
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def mondayDate(self):
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"""
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一年所有的周一日期
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:return:
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"""
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# data_range = pd.date_range(start='2022-01-01', end='2022-12-31', freq='W-Mon')
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data_range = pd.date_range(start='2022-01-01', periods=52, freq='W-Mon')
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print(data_range)
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def dayHourDate(self):
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"""
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一日所有的小时
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:return:
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"""
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# data_range = pd.date_range(start='2022-01-01', periods=24, freq='H')
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data_range = pd.date_range(start='2022-01-01', end='2022-01-02', freq='H', closed='left')
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print(data_range)
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def dateToDataframe(self):
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"""
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日期生成DataFrame
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:return:
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"""
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data_range = pd.date_range(start='2022-5-01', periods=31, freq='D')
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# print(data_range)
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df = pd.DataFrame(data=data_range, columns=['day'])
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df['day_of_year'] = df['day'].dt.dayofyear
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print(df)
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def dateToRandomDataFrame(self):
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"""
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日期随机生成dataframe:均匀 正态 二项分布
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:return:
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"""
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data_range = pd.date_range(start='2022-1-01', periods=1000, freq='D')
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data = {
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'norm': np.random.normal(loc=0, scale=1, size=1000),
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'unifom': np.random.uniform(low=0, high=1, size=1000),
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'binomial': np.random.binomial(n=1, p=0.2, size=1000)
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}
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df = pd.DataFrame(data=data, index=data_range)
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print(df)
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def logHeadLine(self):
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"""
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打印前10行
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:return:
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"""
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data_range = pd.date_range(start='2022-1-01', periods=1000, freq='D')
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data = {
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'norm': np.random.normal(loc=0, scale=1, size=1000),
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'unifom': np.random.uniform(low=0, high=1, size=1000),
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'binomial': np.random.binomial(n=1, p=0.2, size=1000)
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}
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df = pd.DataFrame(data=data, index=data_range)
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print(df.head(10))
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print(df.tail(5))
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def catDataFrame(self):
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"""
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查看dataframe基本信息
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:return:
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"""
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data_range = pd.date_range(start='2022-1-01', periods=1000, freq='D')
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data = {
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'norm': np.random.normal(loc=0, scale=1, size=1000),
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'unifom': np.random.uniform(low=0, high=1, size=1000),
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'binomial': np.random.binomial(n=1, p=0.2, size=1000)
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}
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df = pd.DataFrame(data=data, index=data_range)
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print(df.info())
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print(df.describe())
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def countDataAppear(self):
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"""
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统计数据出现次数
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:return:
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"""
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data_range = pd.date_range(start='2022-1-01', periods=1000, freq='D')
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data = {
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'norm': np.random.normal(loc=0, scale=1, size=1000),
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'unifom': np.random.uniform(low=0, high=1, size=1000),
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'binomial': np.random.binomial(n=1, p=0.2, size=1000)
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}
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df = pd.DataFrame(data=data, index=data_range)
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print(df['binomial'].value_counts())
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def saveCSV(self):
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"""
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保存前一百行存入csv
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:return:
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"""
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data_range = pd.date_range(start='2022-1-01', periods=1000, freq='D')
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data = {
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'norm': np.random.normal(loc=0, scale=1, size=1000),
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'unifom': np.random.uniform(low=0, high=1, size=1000),
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'binomial': np.random.binomial(n=1, p=0.2, size=1000)
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}
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df = pd.DataFrame(data=data, index=data_range)
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df.head(10).to_csv('分布数据前50.csv')
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def csvLoadDataframe(self):
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"""
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csv加载成dataframe
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:return:
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"""
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df = pd.read_csv('分布数据前50.csv', index_col=0)
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print(df.info())
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print(df.head())
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# test = Pandas()
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# test.csvLoadDataframe()
未完待续
文章来源: blog.csdn.net，作者：懿曲折扇情，版权归原作者所有，如需转载，请联系作者。
原文链接：blog.csdn.net/qq_41332844/article/details/126837419
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