DISTINCT column 返回指定列中的唯一值。 Pandas: unique() 方法用于获取指定列中的唯一值。 示例代码: python unique_values = df['column'].unique() 8. HAVING SQL: HAVING condition 用于过滤 GROUP BY 后的结果集。 Pandas: 没有直接对应的 having 方法,但可以使用 groupby() 结合filter() 方法来实现...
import pandas as pd # 创建一个包含列表的DataFrame df = pd.DataFrame({'col1': [1, 2, 3, 2, 1, 3, 4, 5, 4]}) # 提取col1列中的唯一值 unique_values = df['col1'].unique() print(unique_values) 输出结果为: 代码语言:txt 复制 [1 2 3 4 5] 在这个例子中,我们创建了一...
– When working on machine learning or data analysis with Pandas we are often required to get the count of unique or distinct values from a single column or multiple columns. Advertisements You can get the number of unique values in the column of pandas DataFrame using several ways like using...
若一个column具有k个distinct value,我们可以将该column扩展成k个column,每个column表示其中一个value存在与否。我们可以通过函数pandas.get_dummies()来实现该功能,每个column以value命名,通过参数prefix=‘pre’可以将column名前添加pre字符串。当一行属于多个category时,事情变得复杂。 df=pd.DataFrame({'key':['b',...
values (column to aggregate, optional) 用于聚合运算的字段(数据透视的目标变量) index (column, Grouper, array, or list of the previous) 类比于数据透视表中的行标签 columns (column, Grouper, array, or list of the previous) 类比于数据透视表中...
For this purpose, we will use DataFrame['col'].unique() method, it will drop all the duplicates, and ultimately we will be having all the distinct values as a result.Note To work with pandas, we need to import pandas package first, below is the syntax: import pandas as pd ...
total= df.get_value(df.loc[df['tip']==1.66].index.values[0],'total_bill') distinct drop_duplicates根据某列对dataframe进行去重: df.drop_duplicates(subset=['sex'], keep='first', inplace=True) 包含参数: subset,为选定的列做distinct,默认为所有列; ...
reindex(index,column,method):用来重新命名索引,和插值。 size():会返回一个frame,这个frame是groupby后的结果。 sum(n).argsort():如果frame中的值是数字,可以使用sum函数计算frame中摸个属性,各个因子分别求和,并返回一个Series,这个Series可以做为frame.take的参数,拿到frame中对应的行。
This method is used to reshape the given DataFrame according to index and column values. It is used when we have multiple items in a column, we can reshape the DataFrame in such a way that all the multiple values fall under one single index or row, similarly, we can convert these multip...
total = df.loc[df['tip'] ==1.66,'total_bill'].values[0]total = df.get_value(df.loc[df['tip'] ==1.66].index.values[0],'total_bill') distinct drop_duplicates根据某列对dataframe进行去重: df.drop_duplicates(subset=['sex'], keep='first', inplace=True) ...