1 create new column in pandas based on condition 1 create new column based on conditional of other columns 1 How to create another column in pandas based on a condition? 1 Python Create column based on existing column with conditions 4 Create a new column based on a condition 1 Creat...
1 Drop Duplicates based on condition of two columns 0 Drop duplicates where two columns have same values - pandas 2 Drop duplicates with condition 1 Pandas drop_duplicates with multiple conditions 0 Python Pandas drop row duplicates on a column if no duplicate on other column 2 How ...
在上述代码中,column1和column2是数据框中的两列,根据具体需求可以修改为实际的列名。condition是用于分组的条件列名。 最后,new_df是包含新列的更新后的数据框。 这种方法可以根据不同的条件为每个分组创建不同的列,适用于根据条件进行数据处理和分析的场景。
df = pd.DataFrame(data) # Creating a new column 'D' based on a condition in column 'A' df['D'] = df['A'].apply(lambda x: 'Even' if x % 2 == 0 else 'Odd') print(df) Output: A D 0 1 Odd 1 2 Even 2 3 Odd 使用lambda函数来检查' a '中的每个元素是偶数还是奇数,并将...
# Creating a new column 'D' based on a condition in column 'A' df['D'] = df['A'].apply(lambda x: 'Even' if x % 2 == 0 else 'Odd') print(df) Output: A D 0 1 Odd 1 2 Even 2 3 Odd 使用lambda函数来检查' a '中的每个元素是偶数还是奇数,并将结果分配给' D '列。
data={'A':[1,2,3]}df=pd.DataFrame(data)# Creating anewcolumn'D'based on a conditionincolumn'A'df['D']=df['A'].apply(lambda x:'Even'ifx%2==0else'Odd')print(df)Output:AD01Odd12Even23Odd 使用lambda函数来检查' a '中的每个元素是偶数还是奇数,并将结果分配给' D '列。
# 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 based on a condition ...
['likes_count'] > 15) ] # create a list of the values we want to assign for each condition values = ['tier_4', 'tier_3', 'tier_2', 'tier_1'] # create a new column and use np.select to assign values to it using our lists as arguments df['tier'] = np.select(conditions...
np.where(condition1,x1,np.where(condition2,x2,np.where(condition3,x3,...))) 五、自定义函数规范列 接下来就要对列中的字符串进行整理,除了利用循环和.str()方法相结合的方式进行操作,我们还可以选择用applymap()方法,它会将传入的函数作用于整个DataFrame所有行列中的每个元素。
语法:df.loc[ df["column_name"] == "some_value", "column_name" ] = "value" some_value = 需要被替换的值 value = 应该被放置的值。 注意:你也可以使用其他运算符来构建条件,改变数值。 示例: # Importing the librariesimportpandasaspdimportnumpyasnp# dataStudent={'Name':['John','Jay','sac...