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...
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...
# create a dataframedframe = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['India', 'USA', 'China', 'Russia'])#compute a formatted string from each floating point value in framechangefn = lambda x: '%.2f' % x# Make...
(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...
in Series.__getitem__(self, key) 1118 return self._values[key] 1120 elif key_is_scalar: -> 1121 return self._get_value(key) 1123 # Convert generator to list before going through hashable part 1124 # (We will iterate through the generator there to check for slices) 1125 if is_iterato...
我利用pivot和set_index,把不需要处理的columns先暂时设置成index,这样仅仅留下来两列作为新生成的列的column name和value,完成后在reset_index即可。 # 下面是把行转成列 # 提取保持不变的列,未来要暂时作为index index_col = [item for item in df_Tableau.keys() if item not in ['Measurement', 'Data...
on the otheraxes are still respected in the join.keys : sequence, default NoneIf multiple levels passed, should contain tuples. Constructhierarchical index using the passed keys as the outermost level.levels : list of sequences, default NoneSpecific levels (unique values) to use for constructing...
In [8]: pd.Series(d) Out[8]: b1a0c2dtype: int64 如果传递了索引,则将从数据中与索引中的标签对应的值提取出来。 In [9]: d = {"a":0.0,"b":1.0,"c":2.0} In [10]: pd.Series(d) Out[10]: a0.0b1.0c2.0dtype: float64
Series作为行还是作为列主要取决于在构建DataFrame时是使用字典dict(列)还是列表list(行)。 作为列 AI检测代码解析 # 方式一:value是list类型 # dtype指定列的数据类型,注意:只能统一指定,不能按照列来指定 df = pd.DataFrame({'姓名': ['猛男', '舔狗', '细狗'], '年龄': [10, 20, 30], '性别': ...
usecols支持一个回调函数column_check，可通过该函数对数据进行处理。下面是一个简单的示例：def column_check(x):if 'unnamed' in x.lower():return False if 'priority' in x.lower():return False if 'order' in x.lower():return True return True df = pd.read_excel(src_file, header=1, usecols...