从Pandas数据框内部的for循环中创建NumPy数组。

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英文:

Create numpy array from panda daataframe inside a For loop

问题

以下是翻译好的代码部分：

  1. 让我们假设我有以下的数据框：
  3. data = {"Names": ["Ray", "John", "Mole", "Smith", "Jay", "Marc", "Tom", "Rick"],
  4.         "Sports": ["Soccer", "Judo", "Tenis", "Judo", "Tenis","Soccer","Judo","Tenis"]}
  6. 我想要使用一个循环，对于每个独特的运动项目，我可以获取一个包含参与该运动项目的人的numpy数组。在伪代码中，可以解释为：
  8. for unique sport in sports:
  9.     nArray = 包含练习该运动的人的numpy数组
  10.     ---------
  11.     对nArray执行某些操作
  12.     -------

请注意，代码中的伪代码部分没有被翻译，只有注释和字符串被翻译。

英文:

Lets say that i have the following dataframe:

  1. data = {"Names": ["Ray", "John", "Mole", "Smith", "Jay", "Marc", "Tom", "Rick"],
  2.         "Sports": ["Soccer", "Judo", "Tenis", "Judo", "Tenis","Soccer","Judo","Tenis"]}

I want to have a for loop like that for each unique Sport i am able to retrieve a numpy array containing the Names of people playing that sport. In pseudo code that can be explainded as

  1. for unique sport in sports:
  2.     nArray= numpy array of names of people practicing sport
  3.     ---------
  4.     Do something with nArray
  5.     -------

答案1

得分: 0

使用 GroupBy.apply 与 np.array：

  1. df = pd.DataFrame(data)
  2. s = df.groupby('Sports')['Names'].apply(np.array)
  3. print (s)
  4. Sports
  5. Judo      [John, Smith, Tom]
  6. Soccer           [Ray, Marc]
  7. Tenis      [Mole, Jay, Rick]
  8. Name: Names, dtype: object
  10. for sport, name in s.items():
  11.     print (name)
  13.     ['John' 'Smith' 'Tom']
  14.     ['Ray' 'Marc']
  15.     ['Mole' 'Jay' 'Rick']
英文:

Use GroupBy.apply with np.array:

  1. df = pd.DataFrame(data)
  2. s = df.groupby('Sports')['Names'].apply(np.array)
  3. print (s)
  4. Sports
  5. Judo      [John, Smith, Tom]
  6. Soccer           [Ray, Marc]
  7. Tenis      [Mole, Jay, Rick]
  8. Name: Names, dtype: object
  10. for sport, name in s.items():
  11.     print (name)
  13.     ['John' 'Smith' 'Tom']
  14.     ['Ray' 'Marc']
  15.     ['Mole' 'Jay' 'Rick']

答案2

得分: 0

一种方法是

  1. df = pd.DataFrame(data)
  2. for sport in df.Sports.unique():
  3.     list_of_names = list(df[df.Sports == sport].Names)
  4.     data = np.array(list_of_names)
英文:

one way to go

  1. df = pd.DataFrame(data)
  2. for sport in df.Sports.unique():
  3.     list_of_names = list(df[df.Sports == sport].Names)
  4.     data = np.array(list_of_names)

答案3

得分: 0

  1. import numpy as np
  2. import pandas as pd
  4. data = {"Names": ["Ray", "John", "Mole", "Smith", "Jay", "Marc", "Tom", "Rick"],
  5.         "Sports": ["Soccer", "Judo", "Tenis", "Judo", "Tenis", "Soccer", "Judo", "Tenis"]}
  7. df = pd.DataFrame(data)
  9. unique_sports = df['Sports'].unique()
  11. for sport in unique_sports:
  12.     uniqueNames = np.array(df[df['Sports'] == sport]['Names'])
  14. print(uniqueNames)

Result:

['Mole' 'Jay' 'Rick']

英文:

You can do by pandas library for get list array of sport persons name.

  1. import numpy as np
  2. import pandas as pd
  4. data = {"Names": ["Ray", "John", "Mole", "Smith", "Jay", "Marc", "Tom", "Rick"],
  5.         "Sports": ["Soccer", "Judo", "Tenis", "Judo", "Tenis","Soccer","Judo","Tenis"]}
  7. df = pd.DataFrame(data)
  9. unique_sports = df['Sports'].unique()
  11. for sport in unique_sports:
  12.     uniqueNames = np.array(df[df['Sports'] == sport]['Names'])
  14. print(uniqueNames)

Result :

  1. ['Mole' 'Jay' 'Rick']

huangapple
  • 本文由 发表于 2023年2月6日 19:14:31
  • 转载请务必保留本文链接：https://go.coder-hub.com/75360577.html
  • numpy
  • pandas
  • python
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