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...
np.where, condition, if true value, if false value np.where(df.index.isin(idxs),df.index,'') np.log2 + where np.log2(df['value'],where=df['value']>0) where不包括的部分keep 原来的value df.col.where df.index.where(df.index.isin(idxs),'') 用一个df更新另一个df 用df2的内容...
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...
df.iloc[:, where] 下标区间的列(integer) df.iloc[where_i, where_j] indtege行列索引 df.at[label_i, label_j] 通过行列的label来取值 df.iat[i, j] 行列位置来选取 reindex method Select either rows or columns by labels get_value, setvalue methods Select single value by row and column la...
Series s.loc[indexer] DataFrame df.loc[row_indexer,column_indexer] 基础知识 如在上一节介绍数据结构时提到的,使用[](即__getitem__,对于熟悉在 Python 中实现类行为的人)进行索引的主要功能是选择较低维度的切片。以下表格显示了使用[]索引pandas 对象时的返回类型值: 对象类型 选择 返回值类型 Series seri...
In [7]: df.info(memory_usage="deep") <class 'pandas.core.frame.DataFrame'> RangeIndex: 5000 entries, 0 to 4999 Data columns (total 8 columns): # Column Non-Null Count Dtype --- --- --- --- 0 int64 5000 non-null int64 1 float64 5000 non-null float64 2 datetime64[ns] 5000...
You can sort the rows by passing a column name to .sort_values(). In cases where rows have the same value (this is common if you sort on a categorical variable), you may wish to break the ties by sorting on another column. You can sort on multiple columns in this way by passing ...
指定某行列值df[colname].at[i]=value ID列递增填充books['ID'].at[i]=i+1 InStore[Yes|No]循环填充books['InStore'].at[i]='Yes' if i%2 == 0 else 'No' Date时间按天递增填充books['Date'].at[i]=start + timedelta(days=i)
df[column].unique() 1. 查看后 x 行的数据 # Getting last x rows. df.tail(5) 1. 2. 跟head 一样,我们只需要调用 tail 并且传入想要查看的行数即可。注意,它并不是从最后一行倒着显示的,而是按照数据原来的顺序显示。 修改列名 输入新列名即可 ...
df_result # df取子df df_new = df_old[['col1','col2']] # dict生成df df_test = pd.DataFrame({<!-- -->'A':[0.587221, 0.135673, 0.135673, 0.135673, 0.135673], 'B':['a','b','c','d','e'], 'C':[1, 2, 3, 4, 5]}) ...