We can perform certain operations on both rows & column values.By replacing all the values based on a condition, we mean changing the value of a column when a specific condition is satisfied.Replacing all values in a column, based on condition...
# Using str.contains()forfiltering rows df[df['Customer Segment'].str.contains('Office')] 更新值 loc[]:可以为DataFrame中的特定行和列并分配新值。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # Update valuesina column based on a condition df.loc[df['Customer Country']=='United State...
# Using str.contains() for filtering rows df[df['Customer Segment'].str.contains('Office')] 更新值 loc[]:可以为DataFrame中的特定行和列并分配新值。 # Update values in a column based on a condition df.loc[df['Customer Country'] == 'United States', 'Customer Country'] = 'USA' iloc[...
# Using str.contains() for filtering rows df[df['Customer Segment'].str.contains('Office')] 更新值 loc[]:可以为DataFrame中的特定行和列并分配新值。 # Update values in a column based on a condition df.loc[df['Customer Country'] == 'United States', 'Customer Country'] = 'USA' iloc[...
condition=df['Order Quantity']>3df.loc[condition]# or df.loc[df['Order Quantity']>3] 1. 2. 3. 4. 5. 6. 复制 # Using locforfiltering rows df.loc[df['Customer Country']=='United States'] 1. 2. iloc():按位置索引筛选行。
# Using iloc for filtering rowsdf.iloc[:3, :2] []括号操作符:它允许根据条件过滤行。df(条件) #Using[] bracketoperatorforfilteringrows#Using[] bracketoperatorforfilteringrowscondition = df['Order Quantity'] >3df[condition] #ordf[df['Order Quantity'] >3] ...
# Using str.contains() for filtering rowsdf[df['Customer Segment'].str.contains('Office')] 更新值 loc[]:可以为DataFrame中的特定行和列并分配新值。 #Updatevaluesinacolumnbasedonaconditiondf.loc[df['Customer Country']=='United States','Customer Country']='USA' ...
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[False, True, False, True, False, False, False, True, False, True, False, True])# Use extract to get the valuesnp.extract(cond, array)array([ 1, 19, 11, 13, 3])# Apply condition on extract directlynp.extract(((array < 3) | (array >...
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