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(int...
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...
In [12]: df.loc[:, ['B', 'A']] = df[['A', 'B']].to_numpy() In [13]: df[['A', 'B']] Out[13]: A B 2000-01-01 0.469112 -0.282863 2000-01-02 1.212112 -0.173215 2000-01-03 -0.861849 -2.104569 2000-01-04 0.721555 -0.706771 2000-01-05 -0.424972 0.567020 2000-01-0...
scalar_value =7# 定义一个标量值 scalar_index =['x','y','z']# 定义索引 s_from_scalar = pd.Series(scalar_value, index=scalar_index)# 从标量创建Series print(" --- Series from Scalar Value ---") print(s_from_scalar) # 输出: # x 7 # y 7 # z 7 # dtype: int64 # 也可以不...
Python program to select rows whose column value is null / None / nan# Importing pandas package import pandas as pd # Importing numpy package import numpy as np # Creating a dictionary d= { 'A':[1,2,3], 'B':[4,np.nan,5], 'C':[np.nan,6,7] } # Creating DataFrame df = pd...
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]}) ...
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 label ...
>>> df_excel = pd.read_excel('data/table.xlsx') #xls或xlsx格式,需要安装xlrd包 1. 2. 3. 2、写入文件 >>> df.to_csv('data/new_table.csv') # csv格式 >>> df.to_csv('data/new_table.csv', index=False) # 保存时除去行索引 ...
sc= s.value_counts(sort = False) # 也可以这样写:pd.value_counts(sc, sort =False) print(sc) 4.成员资格 # 成员资格:.isin() s= pd.Series(np.arange(10,15)) df= pd.DataFrame({'key1':list('asdcbvasd'),'key2':np.arange(4,13)}) ...
pd.concat([df1,df3],axis = 1) # axis = 1表示列增加,注意进行合并的时候,行索引要一致,否则会出现类似SQL中outer join关联不上的出现None值的情况6.2.insert数据插入 df1.insert(loc = 1, # 插入位置,插入为列索引为1的位置 column='C++', # 插入一列,这一列名字 value = np.random.randint(0,...