如何在熊猫数据框中放置一列或多列
让我们讨论一下如何在熊猫数据框中删除一列或多列。从数据框中删除一列或多列可以通过多种方式实现。
用列表字典创建一个简单的数据框架，比如列名是 A，B，C，D，E 。
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
df
输出:
方法#1: 使用 drop() 方法从数据框中删除列。
删除特定的单列。
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Remove column name 'A'
df.drop(['A'], axis = 1)
输出:
移除特定的多列。
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Remove two columns name is 'C' and 'D'
df.drop(['C', 'D'], axis = 1)
# df.drop(columns =['C', 'D'])
输出:
根据列索引删除列。
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Remove three columns as index base
df.drop(df.columns[[0, 4, 2]], axis = 1, inplace = True)
df
输出:
方法 2: 使用 iloc[] 和drop()方法从数据框中删除列。
删除特定列与另一列之间的所有列。
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Remove all columns between column index 1 to 3
df.drop(df.iloc[:, 1:3], inplace = True, axis = 1)
df
输出:
方法 3: 使用ix()和drop()方法从数据框中删除列。
删除特定列名与另一列名之间的所有列。
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Remove all columns between column name 'B' to 'D'
df.drop(df.ix[:, 'B':'D'].columns, axis = 1)
输出:
方法#4: 使用 loc[] 和drop()方法从数据框中删除列。
删除特定列名与另一列名之间的所有列。
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# Remove all columns between column name 'B' to 'D'
df.drop(df.loc[:, 'B':'D'].columns, axis = 1)
输出:
注:不同loc()和iloc()是iloc()排除最后一列范围元素。
方法#5: 通过迭代方式从数据框中删除列。
删除特定列名与另一列名之间的所有列。
# Import pandas package
import pandas as pd
# create a dictionary with five fields each
data = {
'A':['A1', 'A2', 'A3', 'A4', 'A5'],
'B':['B1', 'B2', 'B3', 'B4', 'B5'],
'C':['C1', 'C2', 'C3', 'C4', 'C5'],
'D':['D1', 'D2', 'D3', 'D4', 'D5'],
'E':['E1', 'E2', 'E3', 'E4', 'E5'] }
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
for col in df.columns:
if 'A' in col:
del df[col]
df
输出:
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