# Update values in a column based on a condition df.loc[df['Customer Country'] == 'United States', 'Customer Country'] = 'USA' iloc[]:也可以为DataFrame中的特定行和列并分配新值,但是他的条件是数字索引 # Update values in a column b
iloc[]:也可以为DataFrame中的特定行和列并分配新值,但是他的条件是数字索引 # Update values in a column based on a condition df.iloc[df['Order Quantity'] > 3, 15] = 'greater than 3' # condition = df['Order Quantity'] > 3 df.iloc[condition, 15] = 'greater than 3' replace():用新...
# Update valuesina column based on a condition df.iloc[df['Order Quantity']>3,15]='greater than 3'# condition=df['Order Quantity']>3df.iloc[condition,15]='greater than 3' replace():用新值替换DataFrame中的特定值。df.['column_name'].replace(old_value, new_value, inplace=True) 代码...
# Update valuesina column based on a condition df.iloc[df['Order Quantity']>3,15]='greater than 3'# condition=df['Order Quantity']>3df.iloc[condition,15]='greater than 3' 1. 2. 3. 4. 5. 6. replace():用新值替换DataFrame中的特定值。df.['column_name'].replace(old_value, new_...
# Update values in a column based on a conditiondf.iloc[df['Order Quantity'] >3,15] = 'greater than3'#condition= df['Order Quantity'] >3df.iloc[condition,15] = 'greater than3' replace():用新值替换DataFrame中的特定值。df.['column_name'].replace(old_value, new_value, inplace=Tru...
Python program to replace all values in a column, based on condition # Importing pandas packageimportpandasaspd# creating a dictionary of student marksd={"Players":['Sachin','Ganguly','Dravid','Yuvraj','Dhoni','Kohli'],"Format":['ODI','ODI','ODI','ODI','ODI','ODI'],"Runs":[15921...
Python program to update value if condition in 3 columns are met# Importing pandas package import pandas as pd # Importing numpy package import numpy as np # Creating a dictionary d = { 'Fruits':['Banana','Apple','pomegranate'], 'Vegetables':['Potato','Soya','BottleGuard'], 'Diet_...
dict, {column_name: default term, column_name: func} df.groupby('Category').agg({'Values1': 'sum', 'Values2': 'mean'})用customize func的速度往往比built in 的速度慢很多很多,比如sum, size等。 简单的算法优先考虑built in 的。如果觉得慢,用GPU加速的RAPIDS里的cudf 和cuml, 参考"%load_ext...
Add Row Based on Presence of NaN Values First, we will add some NaN values to our ‘Monthly_Charge’ column to simulate a typical data issue. import numpy as np df.loc[[2, 5, 9], 'Monthly_Charge'] = np.nan print(df) Output: ...
这将把column_name列按照下划线分隔成两列new_index1和new_index2,并将其添加到DataFrame中。 设置新的索引,可以使用set_index()函数: 代码语言:python 代码运行次数:0 复制Cloud Studio 代码运行 df.set_index(['new_index1', 'new_index2'], inplace=True) 这将把new_index1和new_index2作为新的索引。