Filter by Column Value:To select rows based on a specific column value, use the index chain method. For example, to filter rows where sales are over 300: Pythongreater_than = df[df['Sales'] > 300] This will return rows with sales greater than 300.Filter by Multiple Conditions:...
例如,假设我们希望过滤出名字以 J 开头并且年龄小于 30 的用户。 # 过滤名字以 'J' 开头且年龄小于 30 的用户filtered_df_multiple_conditions=df.filter((df.Name.startswith("J"))&(df.Age<30))# 显示过滤后的 DataFramefiltered_df_multiple_conditions.show() 1. 2. 3. 4. 5. 在这个示例中,我们...
If the filter condition returnsFalse, then it updates the row with the value specified inotherparameter. If the filter condition returnsTrue, then it does not update the row. Example In the below example, we want to replace the student marks with ‘0’ where marks are less than 80. We p...
condition is the criteria used to filter the columns you want to keep. Let’s work again with our DataFrame df and select all the columns except the team column: df_sel = df.select([col for col in df.columns if col != "team"]) Powered By Complex conditions with .selectExpr() If...
buildings_lazy ... .with_columns( ... (pl.col("price") / pl.col("sqft")).alias("price_per_sqft") ... ) ... .filter(pl.col("price_per_sqft") > 100) ... .filter(pl.col("year") < 2010) ... ) >>> lazy_query <polars.LazyFrame object at 0x10B6AF290> In...
DataFrame数据优化(内存优化) 主要就是对一些数据类型进行变换,如 字符串转为布尔型 类别型转为数字型 缺失值处理再类型转换等 浮点型数值转为整数型 单条件获取数据 ==.png != / >.png 多条件提取数据 We can filter a DataFrame with multiple conditions by creating two independent Boolean Series and then...
If you are loading data from Parquet with partitioning on the key that you care about, you should add the filter to theread_parquet. This filter is not an arbitrary expression; rather, it is a tuple of key, operation, value. The key is a string representing the column. The operation is...
Filter with a column expression df1.filter(df1.Sex == 'female').show() +---+---+---+---+ |PassengerId| Name| Sex|Survived| +---+---+---+---+ | 2|Florence|female| 1| | 3| Laina|female| 1| | 4| Lily|female| 1| +---+---+---+---+ Filter with a SQL...
(If you only want to rename specific fields filter on them in your rename function) from nestedfunctions.functions.field_rename import rename def capitalize_field_name(field_name: str) -> str: return field_name.upper() renamed_df = rename(df, rename_func=capitalize_field_name()) Fillna Thi...
filter(df.age < 13).collect() # [Row(age=12,gender='female',name='Alice'), Row(age=11,gender='male',name='Bob')] # Filter by a set of boolean conditions (by &) df.filter((df.age < 13) & (df.gender == 'male')).collect() # Row(age=11,gender='male',name='Bob')] ...