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...
read_csv("data.csv") 数据探索和清洗 # 查看数据集的前几行 df.head() # 查看数据集的基本信息,如列名、数据类型、缺失值等 df.info() # 处理缺失值 df.dropna() # 删除缺失值 df.fillna(value) # 填充缺失值 # 数据转换和处理 df.groupby(column_name).mean() # 按列名分组并...
max_colwidth : int The maximum width in characters of a column in the repr of a pandas data structure. When the column overflows, a "..." placeholder is embedded in the output. [default: 50] [currently: 200] display.max_info_columns : int max_info_columns is used in DataFrame.info...
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...
51. Convert Column DataType Write a Pandas program to convert the datatype of a given column(floats to ints). Sample Solution: Python Code : importpandasaspdimportnumpyasnp exam_data={'name':['Anastasia','Dima','Katherine','James','Emily','Michael','Matthew','Laura','Kevin','Jonas'...
display(r2)# 对象值,二维ndarray数组r3 = df.values.copy()print('属性值:') display(r3) describe/info - 查看数据信息 - 重要 # 查看其属性、概览和统计信息importnumpyasnpimportpandasaspd# 创建 shape(150,3)的二维标签数组结构DataFramedf = pd.DataFrame(data = np.random.randint(0,151,size = (...
传递的索引是一个轴标签列表。因此,这根据data 是的情况分为几种情况: 来自ndarray 如果data是一个 ndarray,则索引必须与data的长度相同。如果没有传递索引,将创建一个具有值[0, ..., len(data) - 1]的索引。 In [3]: s = pd.Series(np.random.randn(5), index=["a","b","c","d","e"]) ...
通过查阅pandas.DataFrame.to_sql的api文档1,可以通过指定dtype 参数值来改变数据库中创建表的列类型。 dtype : dict of column name to SQL type, default None Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type, or a string for sqlite3 fallback connection. 根据...
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...
print("访问第 1 行") print(s_data.loc["one"]) # 访问第 1 行第 2 列元素 print(s_data.loc["one"]["feature_two"]) DataFrame 数据对象的切片语法和 NumPy 数组的切片语法相同。 重新索引 重新索引是 pandas 非常重要的功能,它可以对数据重新建立索引,并且在建立索引的过 程中对缺失值进行填充。