在 Python 中找到熊猫数据框中元素的位置
在本文中，我们将看到如何使用用户定义的函数找到数据框中元素的位置。让我们首先用一个列表字典创建一个简单的数据框，比如列名是:“名称”、“年龄”、“城市”和“部分”。
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# Import pandas library
import pandas as pd
# List of tuples
students = [('Ankit', 23, 'Delhi', 'A'),
('Swapnil', 22, 'Delhi', 'B'),
('Aman', 22, 'Dehradun', 'A'),
('Jiten', 22, 'Delhi', 'A'),
('Jeet', 21, 'Mumbai', 'B')
]
# Creating Dataframe object
df = pd.DataFrame(students, columns =['Name', 'Age', 'City', 'Section'])
df
输出:
示例 1 : 找到数据框中元素的位置。
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# Import pandas library
import pandas as pd
# List of tuples
students = [('Ankit', 23, 'Delhi', 'A'),
('Swapnil', 22, 'Delhi', 'B'),
('Aman', 22, 'Dehradun', 'A'),
('Jiten', 22, 'Delhi', 'A'),
('Jeet', 21, 'Mumbai', 'B')
]
# Creating Dataframe object
df = pd.DataFrame(students, columns =['Name', 'Age', 'City', 'Section'])
# This function will return a list of
# positions where element exists
# in the dataframe.
def getIndexes(dfObj, value):
# Empty list
listOfPos = []
# isin() method will return a dataframe with
# boolean values, True at the positions
# where element exists
result = dfObj.isin([value])
# any() method will return
# a boolean series
seriesObj = result.any()
# Get list of column names where
# element exists
columnNames = list(seriesObj[seriesObj == True].index)
# Iterate over the list of columns and
# extract the row index where element exists
for col in columnNames:
rows = list(result[col][result[col] == True].index)
for row in rows:
listOfPos.append((row, col))
# This list contains a list tuples with
# the index of element in the dataframe
return listOfPos
# Calling getIndexes() function to get
# the index positions of all occurrences
# of 22 in the dataframe
listOfPositions = getIndexes(df, 22)
print('Index positions of 22 in Dataframe : ')
# Printing the position
for i in range(len(listOfPositions)):
print( listOfPositions[i])
输出:
现在让我们理解函数 getIndexes()是如何工作的。 isin() 、 dataframe/series.any() ，接受值并返回一个带有布尔值的 dataframe。该布尔数据帧的大小与第一个原始数据帧的大小相似。如果数据框中存在给定元素，则该值为真，否则为假。然后找到包含元素 22 的列的名称。我们可以通过获取包含 True 的布尔数据框中的列名来实现这一点。现在，在布尔数据框中，我们遍历每一个选定的列，对于每一列，我们找到带有 True 的行。现在，这些列名和行索引的组合(如果存在 True)就是数据框中的索引位置 22。这就是 getIndexes()如何建立给定元素的精确索引位置&以(行，列)元组的形式存储每个位置。最后，它返回一个元组列表，表示它在数据帧中的索引位置。 示例 2: 查找数据框中多个元素的位置。
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# Import pandas library
import pandas as pd
# List of tuples
students = [('Ankit', 23, 'Delhi', 'A'),
('Swapnil', 22, 'Delhi', 'B'),
('Aman', 22, 'Dehradun', 'A'),
('Jiten', 22, 'Delhi', 'A'),
('Jeet', 21, 'Mumbai', 'B')
]
# Creating Dataframe object
df = pd.DataFrame(students, columns =['Name', 'Age', 'City', 'Section'])
# This function will return a
# list of positions where
# element exists in dataframe
def getIndexes(dfObj, value):
# Empty list
listOfPos = []
# isin() method will return a dataframe with
# boolean values, True at the positions
# where element exists
result = dfObj.isin([value])
# any() method will return
# a boolean series
seriesObj = result.any()
# Get list of columns where element exists
columnNames = list(seriesObj[seriesObj == True].index)
# Iterate over the list of columns and
# extract the row index where element exists
for col in columnNames:
rows = list(result[col][result[col] == True].index)
for row in rows:
listOfPos.append((row, col))
# This list contains a list tuples with
# the index of element in the dataframe
return listOfPos
# Create a list which contains all the elements
# whose index position you need to find
listOfElems = [22, 'Delhi']
# Using dictionary comprehension to find
# index positions of multiple elements
# in dataframe
dictOfPos = {elem: getIndexes(df, elem) for elem in listOfElems}
print('Position of given elements in Dataframe are : ')
# Looping through key, value pairs
# in the dictionary
for key, value in dictOfPos.items():
print(key, ' : ', value)
输出:
