Python pandas DataFrame操作的实现代码
1. 从字典创建Dataframe
>>> import pandas as pd >>> dict1 = {'col1':[1,2,5,7],'col2':['a','b','c','d']} >>> df = pd.DataFrame(dict1) >>> df col1 col2 0 1 a 1 2 b 2 5 c 3 7 d
2. 从列表创建Dataframe (先把列表转化为字典,再把字典转化为DataFrame)
>>> lista = [1,2,5,7] >>> listb = ['a','b','c','d'] >>> df = pd.DataFrame({'col1':lista,'col2':listb}) >>> df col1 col2 0 1 a 1 2 b 2 5 c 3 7 d
3. 从列表创建DataFrame,指定data和columns
>>> a = ['001','zhangsan','M'] >>> b = ['002','lisi','F'] >>> c = ['003','wangwu','M'] >>> df = pandas.DataFrame(data=[a,b,c],columns=['id','name','sex']) >>> df id name sex 0 001 zhangsan M 1 002 lisi F 2 003 wangwu M
4. 修改列名,从['id','name','sex']修改为['Id','Name','Sex']
>>> df.columns = ['Id','Name','Sex'] >>> df Id Name Sex 0 001 zhangsan M 1 002 lisi F 2 003 wangwu M
5. 调整DataFrame列顺序、调整列编号从1开始
https://www.jb51.net/article/163644.htm
6. DataFrame随机生成10行4列int型数据
>>> import pandas >>> import numpy >>> df = pandas.DataFrame(numpy.random.randint(0,100,size=(10, 4)), columns=list('ABCD')) # 0,100指定随机数为0到100之间(包括0,不包括100),size = (10,4)指定数据为10行4列,column指定列名 >>> df A B C D 0 67 28 37 66 1 21 27 43 37 2 73 54 98 85 3 40 78 4 93 4 99 60 63 16 5 48 46 24 61 6 59 52 62 28 7 20 74 36 64 8 14 13 46 60 9 18 44 70 36
7. 用时间序列做index名
>>> df # 原本index为自动生成的0~9 A B C D 0 31 25 45 67 1 62 12 61 88 2 79 36 20 97 3 26 57 50 44 4 24 12 50 1 5 4 61 99 62 6 40 47 52 27 7 83 66 71 4 8 58 59 25 62 9 38 81 60 8 >>> import pandas >>> dates = pandas.date_range('20180121',periods=10) >>> dates # 从20180121开始,共10天 DatetimeIndex(['2018-01-21', '2018-01-22', '2018-01-23', '2018-01-24', '2018-01-25', '2018-01-26', '2018-01-27', '2018-01-28', '2018-01-29', '2018-01-30'], dtype='datetime64[ns]', freq='D') >>> df.index = dates # 将dates赋值给index >>> df A B C D 2018-01-21 31 25 45 67 2018-01-22 62 12 61 88 2018-01-23 79 36 20 97 2018-01-24 26 57 50 44 2018-01-25 24 12 50 1 2018-01-26 4 61 99 62 2018-01-27 40 47 52 27 2018-01-28 83 66 71 4 2018-01-29 58 59 25 62 2018-01-30 38 81 60 8
8. dataframe 实现类SQL操作
pandas官方文档 Comparison with SQL
https://pandas.pydata.org/pandas-docs/stable/comparison_with_sql.html
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持我们。
赞 (0)