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...
同时Pandas还可以使用复杂的自定义函数处理数据，并与numpy、matplotlib、sklearn、pyspark、sklearn等众多科...
In [32]: %%time ...: files = pathlib.Path("data/timeseries/").glob("ts*.parquet") ...: counts = pd.Series(dtype=int) ...: for path in files: ...: df = pd.read_parquet(path) ...: counts = counts.add(df["name"].value_counts(), fill_value=0) ...: counts.astype(in...
(self, key, value) 1284 ) 1285 1286 check_dict_or_set_indexers(key) 1287 key = com.apply_if_callable(key, self) -> 1288 cacher_needs_updating = self._check_is_chained_assignment_possible() 1289 1290 if key is Ellipsis: 1291 key = slice(None) ~/work/pandas/pandas/pandas/core/seri...
楔子Python 在数据处理领域有如今的地位,和 Pandas 的存在密不可分,然而除了 Pandas 之外,还有一个库也在为 Python 的数据处理添砖加瓦,它就是我们本次要介绍的 Polars。和 Pandas 相比,Polars 的速度更快,执行常见运算的速度是 Pandas 的 5 到
indexs = df.loc[df.duplicated(keep='last')].index 删除重复元素的行 df.drop(labels=indexs,axis=0) 1.2 使用drop_duplicates()函数删除重复的行 drop_duplicates(keep='first/last'/False) df.drop_duplicates(keep='last') 2. 映射 2.1 replace()函数:替换元素 ...
missing values in the dataset with a specific valuedf = df.fillna(0)# Replace missing values in the dataset with mediandf = df.fillna(df.median())# Replace missing values in Order Quantity column with the mean of Order Quantitiesdf['Order Quantity'].fillna(df["Order Quantity"].mean, in...
(inplace=True)# 排序后生效,改变原数据# 索引重新0-(n-1)排,很有用,可以得到它的排序号s.sort_index(ignore_index=True)s.sort_index(na_position='first')# 空值在前,另'last'表示空值在后s.sort_index(level=1)# 如果多层,排一级s.sort_index(level=1, so...
(most recent call last) Cell In[27], line 1 ---> 1 df.apply(f, axis="columns") File ~/work/pandas/pandas/pandas/core/frame.py:10374, in DataFrame.apply(self, func, axis, raw, result_type, args, by_row, engine, engine_kwargs, **kwargs) 10360 from pandas.core.apply import fr...
# By setting the 'engine' in the DataFrame 'to_excel()' methods.df.to_excel("path_to_file.xlsx", sheet_name="Sheet1", engine="xlsxwriter")# By setting the 'engine' in the ExcelWriter constructor.writer = pd.ExcelWriter("path_to_file.xlsx", engine="xlsxwriter")# Or via pandas ...