Given a Pandas DataFrame, we have to filter rows by regex. Submitted byPranit Sharma, on June 02, 2022 Pandas is a special tool which allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. Data...
duplicated([subset, keep]) #Return boolean Series denoting duplicate rows, optionally only DataFrame选取以及标签操作 代码语言:javascript 代码运行次数:0 运行 AI代码解释 DataFrame.equals(other) #两个数据框是否相同 DataFrame.filter([items, like, regex, axis]) #过滤特定的子数据框 DataFrame.first(...
import polars as pl import time # 读取 CSV 文件 start = time.time() df_pl = pl.read_csv('test_data.csv') load_time_pl = time.time() - start # 过滤操作 start = time.time() filtered_pl = df_pl.filter(pl.col('value1') > 50) filter_time_pl = time.time() - start # 分组...
2013-01-03 2013-01-03 00:00:00-05:00 2013-01-01 00:00:00.000000002 [3 rows x 9 columns] # we preserve dtypes In [612]: result.dtypes Out[612]: a object b int64 c uint8 d float64 e bool f category g datetime64[ns] h datetime64[ns, US/Eastern] i datetime64[ns] dtype: ...
5)使用filter()过滤分组 importpandasaspd# 创建示例 DataFramedata = {'Category': ['A','B','A','B','A','B'],'Value': [10,20,30,40,50,60]} df = pd.DataFrame(data)# 过滤掉 Value 总和小于 50 的分组filtered = df.groupby('Category').filter(lambdax: x['Value'].sum() >50) ...
filter() Filter the DataFrame according to the specified filter first() Returns the first rows of a specified date selection floordiv() Divides the values of a DataFrame with the specified value(s), and floor the values ge() Returns True for values greater than, or equal to the specified ...
df.filter(items=['Q1', 'Q2']) # 选择两列 df.filter(regex='Q', axis=1) # 列名包含Q的列 df.filter(regex='e$', axis=1) # 以e结尾的列 df.filter(regex='1$', axis=0) # 正则,索引名以1结尾 df.filter(like='2', axis=0) # 索引中有2的 # 索引中以2开头、列名有Q的 df.fil...
na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipf...
.filter(pl.col("Category").is_in(["A","B"])) ) 如果表达式是 Eager 执行,则会多余地对整个 DataFrame 执行 groupby 运算,然后按 Category 筛选。 通过惰性执行,DataFrame 会先经过筛选,并仅对所需数据执行 groupby。 4)表达性 API 最后,Polars 拥有一个极具表达性的 API,基本上你想执行的任何运算都...
2.2Using filter() 3When to Use query method? 4When to Use filter method? Filter Rows Using query Method Here’s a basic example: import pandas as pd data = {'user_id': [1, 2, 3, 4], 'age': [24, 30, 22, 26], 'plan_type': ['Basic', 'Premium', 'Basic', 'Premium']}...