2. PySpark Groupby on Multiple Columns Grouping on Multiple Columns in PySpark can be performed by passing two or more columns to the groupBy() method, this returns apyspark.sql.GroupedDataobject which contains agg(), sum(), count(), min(), max(), avg() e.t.c to perform aggregations....
print('***整体变化:') print(DF_temp.groupby().agg({'deposit_increase':'sum'}).collect()) print('***存款人均变化:') print(DF_temp.groupby().agg({'deposit_increase':'mean'}).collect())
在pandas库中实现Excel的数据透视表效果通常用的是df['a'].value_counts()这个函数，表示统计数据框(...
df.loc[:,['周','支付金额/¥']].groupby('周').sum().sort_values(by='支付金额/¥',ascending=False) df.loc[:,['level','周','支付金额/¥']].groupby(['周','level']).sum() result_level = df.loc[:,['level','周','支付金额/¥']].groupby(['周','level']).sum() ...
df.agg({'height': 'sum','age': 'sum','weight': 'sum'}).collect() Output: [Row(sum(height)=21.65, sum(age)=92, sum(weight)=200)] In the above example, the total value (sum) from the height, age, and weight columns is returned. Method 3: Using groupBy() method We can get...
groupBy().max('air_time').show() # Average duration of Delta flights flights.filter(flights.carrier == "DL").filter(flights.origin == "SEA").groupBy().avg("air_time").show() # Total hours in the air flights.withColumn("duration_hrs", flights.air_time/60).groupBy().sum("duration...
df %>% group_by(group) %>% summarise(sum_money = sum(money)) 请您参考如下方法: 虽然我仍然更喜欢dplyr语法,但此代码片段可以: import pyspark.sql.functions as sf (df.groupBy("group") .agg(sf.sum('money').alias('money')) .show(100)) ...
3. Using Multiple columns Similarly, we can also run groupBy and aggregate on two or more DataFrame columns, below example does group by ondepartment,stateand does sum() onsalaryandbonuscolumns. # GroupBy on multiple columns df.groupBy("department","state") \ ...
To find the total amount spent by each customer overall, we just need to group by the CustomerID column and sum the total amount spent: m_val = m_val.groupBy('CustomerID').agg(sum('TotalAmount').alias('monetary_value')) Run code Powered By Merge this dataframe with the all the ot...
To summarize or aggregate a dataframe, first I need to convert the dataframe to a GroupedData object with groupby(), then call the aggregate functions. gdf2 = df2.groupby('Pclass') gdf2 <pyspark.sql.group.GroupedData at 0x9bc8f28> I can take the average of columns by passing an un...