随笔分类 -  数据分析

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给数组赋值nan
摘要：DataFrame和Serious类型可以直接赋值： df = pd.DataFrame(np.arange(12).reshape(3, 4), index=list("abc"), columns=list("WXYZ")) df.loc[["a", "b"], ["W", "Z"]]=np.n 阅读全文
posted @ 2020-03-20 00:43 胡辣汤王子 阅读(698) 评论(0) 推荐(0)
loc和iloc的区别
摘要：import pandas as pd import numpy as np df = pd.DataFrame(np.arange(10).reshape(5,2),index=list("cvbnm"),columns=list('AB')) print(df) print(" ") print 阅读全文
posted @ 2020-03-16 21:15 胡辣汤王子 阅读(403) 评论(0) 推荐(0)
02-matplotlib解决中文乱码
摘要： 阅读全文
posted @ 2020-03-09 20:54 胡辣汤王子 阅读(114) 评论(0) 推荐(0)
01-matplotlib简单绘图
摘要：一、原生绘图案例 二、结合numpy 阅读全文
posted @ 2020-03-09 16:12 胡辣汤王子 阅读(150) 评论(0) 推荐(0)
22-pandas杂碎方法
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posted @ 2020-03-09 14:05 胡辣汤王子 阅读(92) 评论(0) 推荐(0)
21-pandas_apply和transform
摘要：import numpy as np import pandas as pd #1.聚合一次 df=pd.DataFrame({"age":[18,20,22,22,23,23], "name":["A","B","C","D","E","F"], "price1":[1000,900,800,70 阅读全文
posted @ 2020-03-09 12:46 胡辣汤王子 阅读(219) 评论(0) 推荐(0)
20-pandas聚合
摘要：import numpy as np import pandas as pd #1.聚合一次 df=pd.DataFrame({"age":[18,20,22,22,23,23], "name":["A","B","C","D","E","F"], "price1":[1000,900,800,70 阅读全文
posted @ 2020-03-09 12:30 胡辣汤王子 阅读(126) 评论(0) 推荐(0)
19-pandas排序和随机抽样
摘要：import numpy as np import pandas as pd df=pd.DataFrame(np.arange(25).reshape(5,5)) new_order=np.random.permutation(5)#不暗中哦顺序排列 print(df.take(new_order 阅读全文
posted @ 2020-03-09 12:03 胡辣汤王子 阅读(173) 评论(0) 推荐(0)
18-pandas数据处理的高级功能
摘要：import numpy as np import pandas as pd import tushare as ts df=pd.DataFrame(np.random.randn(1000,4)) print(df.head()) print(df.describe())#列出一些信息 prin 阅读全文
posted @ 2020-03-09 11:53 胡辣汤王子 阅读(270) 评论(0) 推荐(0)
17-pandas处理数据的简单功能
摘要：import numpy as np import pandas as pd import tushare as ts #1. df=pd.DataFrame({"num":[1,2,3,4,3,2,1,4], "id":["A","B","C","D","C","B","A","D"]}) pri 阅读全文
posted @ 2020-03-09 11:43 胡辣汤王子 阅读(165) 评论(0) 推荐(0)
16-pandas数据归并几对几
摘要：import numpy as np import pandas as pd #1.一对一 df1=pd.DataFrame({"name":["A","B","C"], "age":[30,32,33]}) df2=pd.DataFrame({"name":["A","B","C"], "tall 阅读全文
posted @ 2020-03-09 11:06 胡辣汤王子 阅读(173) 评论(0) 推荐(0)
15-pandas与numpy加法差异
摘要：import numpy as np import pandas as pd a=np.array([1,2,3]) b=np.array([3]) print(a+b)#[4 5 6] 只要列一致，广播，每个都加 s=pd.Series([1,2,3,4]) print(s+1)#Series与数 阅读全文
posted @ 2020-03-09 01:27 胡辣汤王子 阅读(217) 评论(0) 推荐(0)
14-pandas左右归并
摘要：import numpy as np import pandas as pd 1. df1=pd.DataFrame({"name":["wangchenyang","guanchenhao","dongshuai"], "age":[30,32,33]}) df2=pd.DataFrame({"n 阅读全文
posted @ 2020-03-09 00:39 胡辣汤王子 阅读(225) 评论(0) 推荐(0)
13-pandas数据归并
摘要：import numpy as np import pandas as pd #1.完全匹配 df1=pd.DataFrame({"name":["wangchenyang","guanchenhao","dongshuai"], "age":[30,32,33]}) df2=pd.DataFram 阅读全文
posted @ 2020-03-09 00:38 胡辣汤王子 阅读(192) 评论(0) 推荐(0)
12-pandas不匹配拼接
摘要：import pandas as pd def make_dataFrame(cols,ind): data={c:[str(c)+str(i) for i in ind] for c in cols} return pd.DataFrame(data,ind) x=make_dataFrame(" 阅读全文
posted @ 2020-03-09 00:36 胡辣汤王子 阅读(220) 评论(0) 推荐(0)
11-pandas拼接
摘要：import numpy as np import pandas as pd x=np.array([1,2,3]).reshape(1,3)#调节形状为二维数组 y=np.array([4,5,6]).reshape(1,3) z=np.array([7,8,9]).reshape(1,3) pr 阅读全文
posted @ 2020-03-09 00:35 胡辣汤王子 阅读(116) 评论(0) 推荐(0)
10-pandas多层索引操作
摘要：import numpy as np import pandas as pd index=[("beijing",2007),("beijing", 2017), ("beijing", 2027),("shenzhen",2007), ("shenzhen",2017),("shenzhen",2 阅读全文
posted @ 2020-03-08 16:07 胡辣汤王子 阅读(329) 评论(0) 推荐(0)
09-pandas多层索引切片
摘要：import numpy as np import pandas as pd index=[("beijing",2007),("beijing", 2017), ("beijing", 2027),("shenzhen",2007), ("shenzhen",2017),("shenzhen",2 阅读全文
posted @ 2020-03-08 16:05 胡辣汤王子 阅读(626) 评论(0) 推荐(0)
08-pandas嵌套索引
摘要：import numpy as np import pandas as pd #1.风格1 df=pd.DataFrame(np.random.rand(4,2), columns=["A","B"], index=[["1","1","2","2"], ["a","b","a","b"]]) pr 阅读全文
posted @ 2020-03-08 16:03 胡辣汤王子 阅读(305) 评论(0) 推荐(0)
07-pandas双索引
摘要：import numpy as np import pandas as pd from matplotlib import pyplot as plt nasa=pd.read_table(r"Y:\Workplace\BigData\percent-bachelors-degrees-women- 阅读全文
posted @ 2020-03-08 13:13 胡辣汤王子 阅读(327) 评论(0) 推荐(0)

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