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.

1. Download URL: https://www.anaconda.com/

2. 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

The above is the detailed content of Series using Pandas data analysis. For more information, please follow other related articles on the PHP Chinese website!