out= df.filter(pl.col("nrs") >2) print(out) out= df.group_by("groups").agg( pl.sum("nrs"),# sum nrs by groups pl.col("random").count().alias("count"),# count group members # sum random where name != null pl.col("random").filter(p...
在操作数据时,Polars使用表达式(expression)和上下文(context)来实现,表达式是对数据表一部分数据进行选中和修改的方法,上下文像一个麻袋一样装着表达式。 上下文有select,with_columns,filter,group_by几种下面是它们各自的作用,数据表在文章开头 select只用于数据,也可以同时对所选数据进行修改,其实作用就相当于pandas里...
DataFrame(data) # 使用表达式进行选择 selected_df = df.select(['column1']) # 使用表达式进行过滤 filtered_df = df.filter(df['column1'] > 1) selected_df filtered_df Join 代码语言:javascript 代码运行次数:0 运行 AI代码解释 df = pl.DataFrame( { "a": np.arange(0, 8), "b": np....
alias("avg_a_by_type_combination"), #partition by多列 ) 7.按行操作fold() out = df.filter( pl.fold( acc=pl.lit(True), function=lambda acc, x: acc & x, exprs=pl.col("*") > 1, #改行的所有行必须全大于一,返回的值才为True,进而该行会被filter筛选出来 ) ) 8.其他操作 # Polars...
df = pl.DataFrame(data) # 使用表达式进行选择 selected_df = df.select(['column1']) # 使用表达式进行过滤 filtered_df = df.filter(df['column1'] > 1) selected_df filtered_df 拼接df = pl.DataFrame( { "a": np.arange(0, 8),
import polars as pl# 创建一个简单的 DataFrame data = {'column1': [1, 2, 3], 'column2': ['a', 'b', 'c']} df = pl.DataFrame(data)# 使用表达式进行选择 selected_df = df.select(['column1'])# 使用表达式进行过滤 filtered_df = df.filter(df['column1'] > 1) selected_df filte...
out = df.group_by('groups').agg( pl.sum('nrs'), # sum nrs by groups pl.col('random').count().alias('count'), # count group members # sum random where name != null pl.col('random').filter(pl.col('names').is_not_null()).sum().name.suffix('_sum'), pl.col('names')...
# 将CSV文件加载到DataFrame中 df_csv = spark.read.csv("data/people.csv", header=True, inferSchema=True) # 显示DataFrame的前几行数据 df_csv.show() # 筛选出年龄大于30的人 df_filtered = df_csv.filter(df_csv["Age"] > 30) # 显示筛选后的DataFrame df_filtered.show() # 按年龄分组并统计...
Thegroupby(['Category', 'SubCategory']).sum()groups the data by both 'Category' and 'SubCategory' and calculates the sum of the 'Values' column. This is useful for multi-level analysis. GroupBy: Filter Groups This example shows how to filter groups based on a condition. ...
("nrs_sum"), pl.col("random").count().alias("count"), ) print(df) out = df.filter(pl.col("nrs") > 2) print(out) out = df.group_by("groups").agg( pl.sum("nrs"), # sum nrs by groups pl.col("random").count().alias("count"), # count group members # sum random ...