in Flags.allows_duplicate_labels(self, value) 94 if not value: 95 for ax in obj.axes: ---> 96 ax._maybe_check_unique() 98 self._allows_duplicate_labels = value File ~/work/pandas/pandas/pandas/core/indexes/base.py:715, in Index._maybe_check_unique(...
dtype="string[pyarrow]") In [10]: ser_ad = pd.Series(data, dtype=pd.ArrowDtype(pa.string())) In [11]: ser_ad.dtype == ser_sd.dtype Out[11]: False In [12]: ser_sd.str.contains("a") Out[12]: 0 True 1 False 2 False dtype: boolean In [13]: ser_...
import polars as pl import time # 读取 CSV 文件 start = time.time() df_pl_gpu = pl.read_csv('test_data.csv') load_time_pl_gpu = time.time() - start # 过滤操作 start = time.time() filtered_pl_gpu = df_pl_gpu.filter(pl.col('value1') > 50) filter_time_pl_gpu = time.t...
For this purpose, we will use a simple python keywords 'in' & 'notin'. These keywords are used to check whether a value is present in a series or collection or not. Let us understand with the help of an example, Python program to determine whether a Pandas Column contains a particular...
check_conditions(row): # 条件1：年龄>30，分数>80，性别为男性 result = 'A' if (row...
-->109setattr(self, key, value) File ~/work/pandas/pandas/pandas/core/flags.py:96,inFlags.allows_duplicate_labels(self, value)94ifnotvalue:95foraxinobj.axes: --->96ax._maybe_check_unique()98self._allows_duplicate_labels = value ...
For this purpose, we will first check if a column contains a NaN value or not by using theisna()method and then we will collect all the names of the column containingNaNvalues into a list by using thetolist()method. Note To work with pandas, we need to importpandaspackage first,...
fmt: Series类型,包含每个数据值的数据类型,index为列名,value为类型,其中,object类型相当于Python中的string 2.3.1.2 columns属性 属性调用: index_name = df.columns 属性功能:返回数据结构中每列的列名 属性参数: index_name Index_name: Index类型,<class 'pandas.core.indexes.base.Index'>,包含每列的列名 ...
any errors raised during the downcastingwill be surfaced regardless of the value of the 'errors' input.In addition, downcasting will only occur if the sizeof the resulting data's dtype is strictly larger thanthe dtype it is to be cast to, so if none of the dtypeschecked satisfy that spec...
Upper bounds outliers will be on a blue scale, where the darker blues will be closer to the minimum value for the column. ⭐ will be prepended to any column header which contains outliers. Highlight Range Highlight any range of numeric cells based on three different criteria: equals greater...