根据条件选择熊猫数据框中的行
原文:https://www.geesforgeks.org/select-row-in-pandas-data frame-基于条件/
让我们看看如何根据熊猫数据框中的一些条件选择行。
使用'>', '=', '=', '<=', '!=' 运算符基于特定的列值选择行。
代码#1 : 使用基本方法从给定数据框中选择“百分比”大于 80 的所有行。
# importing pandas
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78] }
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])
print("Given Dataframe :\n", dataframe)
# selecting rows based on condition
rslt_df = dataframe[dataframe['Percentage'] > 80]
print('\nResult dataframe :\n', rslt_df)
输出:
代码#2 : 使用[loc[]](https://www.geeksforgeeks.org/python-pandas-extracting-rows-using-loc/)从给定数据框中选择“百分比”大于 80 的所有行。
# importing pandas
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])
print("Given Dataframe :\n", dataframe)
# selecting rows based on condition
rslt_df = dataframe.loc[dataframe['Percentage'] > 80]
print('\nResult dataframe :\n', rslt_df)
输出:
代码#3 : 使用[loc[]](https://www.geeksforgeeks.org/python-pandas-extracting-rows-using-loc/)从给定数据框中选择“百分比”不等于 95 的所有行。
# importing pandas
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])
print("Given Dataframe :\n", dataframe)
# selecting rows based on condition
rslt_df = dataframe.loc[dataframe['Percentage'] != 95]
print('\nResult dataframe :\n', rslt_df)
输出:
使用数据框的[isin()](https://www.geeksforgeeks.org/python-pandas-dataframe-isin/)方法选择那些列值出现在列表中的行。
代码#1 : 使用基本方法从给定数据帧中选择选项列表中出现“流”的所有行。
# importing pandas
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])
print("Given Dataframe :\n", dataframe)
options = ['Math', 'Commerce']
# selecting rows based on condition
rslt_df = dataframe[dataframe['Stream'].isin(options)]
print('\nResult dataframe :\n', rslt_df)
输出:
代码#2 : 使用[loc[]](https://www.geeksforgeeks.org/python-pandas-extracting-rows-using-loc/)从给定数据帧中选择选项列表中出现“流”的所有行。
# importing pandas
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])
print("Given Dataframe :\n", dataframe)
options = ['Math', 'Commerce']
# selecting rows based on condition
rslt_df = dataframe.loc[dataframe['Stream'].isin(options)]
print('\nResult dataframe :\n', rslt_df)
输出:
代码#3 : 使用.loc[]从给定数据帧中选择选项列表中不存在“流”的所有行。
# importing pandas
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])
print("Given Dataframe :\n", dataframe)
options = ['Math', 'Science']
# selecting rows based on condition
rslt_df = dataframe.loc[~dataframe['Stream'].isin(options)]
print('\nresult dataframe :\n', rslt_df)
输出:
使用'&'运算符基于多列条件选择行。
代码#1 : 使用基本方法从给定数据框中选择“年龄”等于 21 且“流”出现在选项列表中的所有行。
# importing pandas
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])
print("Given Dataframe :\n", dataframe)
options = ['Math', 'Science']
# selecting rows based on condition
rslt_df = dataframe[(dataframe['Age'] == 21) &
dataframe['Stream'].isin(options)]
print('\nResult dataframe :\n', rslt_df)
输出:
代码#2 : 使用从给定数据框中选择“年龄”等于 21 且“流”出现在选项列表中的所有行。loc[]。
# importing pandas
import pandas as pd
record = {
'Name': ['Ankit', 'Amit', 'Aishwarya', 'Priyanka', 'Priya', 'Shaurya' ],
'Age': [21, 19, 20, 18, 17, 21],
'Stream': ['Math', 'Commerce', 'Science', 'Math', 'Math', 'Science'],
'Percentage': [88, 92, 95, 70, 65, 78]}
# create a dataframe
dataframe = pd.DataFrame(record, columns = ['Name', 'Age', 'Stream', 'Percentage'])
print("Given Dataframe :\n", dataframe)
options = ['Math', 'Science']
# selecting rows based on condition
rslt_df = dataframe.loc[(dataframe['Age'] == 21) &
dataframe['Stream'].isin(options)]
print('\nResult dataframe :\n', rslt_df)
**输出:**
