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_...
另外 read_excel 还支持同时读取多个 sheet,只需要给 sheet_id 传一个列表即可。 importpolarsaspl# 我们这里只有一个 sheet# 此时会返回一个字典,key 是 sheet 的名称,value 是对应的 DataFramedf_dict = pl.read_excel("girl.xlsx", sheet_id=[1])print(df_dict.__class__)# <class 'dict'># 每个...
In [432]: df.columns = pd.MultiIndex.from_product([["a"], ["b", "d"]], names=["c1", "c2"])In [433]: df.to_excel("path_to_file.xlsx")In [434]: df = pd.read_excel("path_to_file.xlsx", index_col=[0, 1], header=[0, 1])In [435]: dfOut[435]:c1 ac2 b dlvl...
stack:DataFrame.stack(level=-1, dropna=True),将column变成index,类似把横放的书籍变成竖放 level=-1代表多层索引的最内层,可以通过==0、1、2指定多层索引的对应层 unstack:DataFrame.unstack(level=-1, fill_value=None),将index变成column,类似把竖放的书籍变成横放 pivot:DataFrame.pivot(index=None, columns...
In [10]: ser_ad = pd.Series(data, dtype=pd.ArrowDtype(pa.string())) In [11]: ser_ad.dtype == ser_sd.dtype Out[11]:FalseIn [12]: ser_sd.str.contains("a") Out[12]:0True1False2Falsedtype: boolean In [13]: ser_ad.str.contains("a") ...
def createBonusColumn(employees: pd.DataFrame) -> pd.DataFrame: employees['bonus'] = employees['salary'] * 2 return employees def modifySalaryColumn(employees: pd.DataFrame) -> pd.DataFrame: employees['salary'] = employees['salary'] * 2 ...
For getting a value explicitlyFor getting fast access to a scalar (equivalent to the prior method)// Boolean IndexingUsing a single column’s values to select data.Selecting values from a DataFrame where a boolean condition is met.Using the isin( ) method for filtering:...
# Pivoted DataFrame: Access the USD cost of Item0 for Gold customers print(p[p.index=='Item0'].Gold.values) 需要注意的是,该数据透视表中没有包含欧元价格的任何信息。事实上,数据透视表是原始表格的简化版本,它只包含我们所关心的变量信息。
# Alternatively, you can access a column by passing in a string variable. #col_name = "NDB_No" #ndb_col = food_info[col_name] #columns = ["Zinc_(mg)", "Copper_(mg)"] #zinc_copper = food_info[columns] #print zinc_copper ...
Since the Name column is the 0’th column, the Grades column will have the numerical index value of 3. We can also access multiple columns at once using the loc function by providing an array of arguments, as follows: Report_Card.loc[:,["Lectures","Grades"]] ...