Series using Pandas data analysis
1. Tool preparation
#A good tool for data analysis: anaconda. This tutorial is about using the jupyter tool of anaconda3 in the win10 system. , a tool that runs in a browser.
Download URL: https://www.anaconda.com/
Startup method
Start menu, open the anaconda prompt command line window
Enter the directory where the project is located, and set the directory yourself
Use the command jupyter notebook to open the browser
2. Series type
Once the index is created, the value inside cannot be modified individually
1. Create a Series object
Create an object through a list or array
import pandas as pd import numpy as np users=['张三','李四','王老五'] series1=pd.Series(users) print(series1)
The result of the above code:
0 张三 1 李四 2 王老五 dtype: object
Creating a series object through a dictionary
users={'张三':20,'李四':25,'王五':21}
series2=pd.Series(users)
print(series2)The result of the above code:
张三 20 李四 25 王五 21 dtype: int64
2. Get the sequence of the Series
print(series2.index)
The result of the above code:
Index(['张三', '李四', '王五'], dtype='object')
3. Get the value of the Series
print(series2.values)
The result of the above code:
[20 25 21]
4. Get a certain value
print(series2.values) print(series2[1]) print(series2['王五'])
The result of the above code:
25 21
The above two methods You can get the value of the Series
5. Date and time index
pd.date_range('2022-10-01',periods=4,freq='M')
periods: divided into multiple intervals
freq: divided by year, month, day, week, time, etc.
6. Time interval index
pd.TimedeltaIndex([10,12,14,16],unit="D")
The result of the above code:
TimedeltaIndex(['10 days', '12 days', '14 days', '16 days'], dtype='timedelta64[ns]', freq=None)
The value of unit can be changed to Y, W, H, etc.
7.索引取值
import numpy as np import pandas as pd pd=pd.DataFrame(np.random.randint(1,100,(4,5)),index=['A','B','C','D']) # pd['A':'C']#通过索引名称取值，结果包含最后一个 pd[0:3]#通过索引下标取值，结果不包含最后一个
8. 条件索引
conditon=series>50 series[conditon] 或 series[series>50]
以上代码结果：
0 1 2 3 4 A 84.0 63.0 76.0 72.0 77.0 B NaN 96.0 NaN 65.0 NaN C NaN NaN NaN 81.0 NaN D 74.0 89.0 NaN NaN 53.0
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