In [151]: df = pd.DataFrame( ...: data={ ...: "Province": ["ON", "QC", "BC", "AL", "AL", "MN", "ON"], ...: "City": [ ...: "Toronto", ...: "Montreal", ...: "Vancouver", ...: "Calgary", ...: "Edmonton", ...: "Winnipeg", ...: "Windsor", ...:...
-> 1690 raise RuntimeError( 1691 "Cannot set name on a level of a MultiIndex. Use " 1692 "'MultiIndex.set_names' instead." 1693 ) 1694 maybe_extract_name(value, None, type(self)) 1695 self._name = value RuntimeError: Cannot set name on a level of a MultiIndex. Use 'MultiIndex.s...
使用read_excel命令导入数据，写入路径即可导入数据，数据包含日期、订单号、区域、省份等数据字段。import...
import pandas as pd sdata = {'Ohio':35000,'Texax':71000,'Oregon':16000,'Utah':5000} states = ['California','Ohio','Oregon','Texax'] obj3 = pd.Series(sdata) print(obj3) obj4 = pd.Series(sdata,index = states) # 将有索引的赋值,否则为空 print(obj4) pd.isnull(obj4) # 为...
In [1]: import pandas as pd In [2]: import numpy as np 大多数示例将利用 pandas 测试中找到的 tips 数据集。我们将数据读入一个名为 tips 的DataFrame,并假设我们有一个具有相同名称和结构的数据库表。 In [3]: url = ( ...: "https://raw.githubusercontent.com/pandas-dev" ...: "/pand...
通过Categorical.reorder_categories()和Categorical.set_categories()方法可以重新排序类别。对于Categorical.reorder_categories(),所有旧类别必须包含在新类别中,不允许有新类别。这将必然使排序顺序与类别顺序相同。 In [102]: s = pd.Series([ 1, 2, 3, 1], dtype="category")In [103]: s = s.cat.reor...
# 从数组创建DataFrame# 数组(array)importnumpyasnp d = np.array([[1,2,3],[4,5,6],[7,8,9]]) df2 = pd.DataFrame( data = d, index=["a","b","c"], columns=["A","B","C"] ) df2 数据查看 importnumpyasnpimportpandasaspd ...
import pandas as pd from pyspark.sql.functions import col, pandas_udf from pyspark.sql.types import LongType # Declare the function and create the UDF def multiply_func(a: pd.Series, b: pd.Series) -> pd.Series: return a * b multiply = pandas_udf(multiply_func, returnType=LongType()...
我们“稀疏化”了索引的较高级别,以使控制台输出更加舒适。请注意,可以使用pandas.set_options()中的multi_sparse选项来控制索引的显示方式: In [21]:withpd.option_context("display.multi_sparse",False): ...: df ...: 值得记住的是,没有什么可以阻止你在轴上使用元组作为原子标签: In ...
# 重设索引 print(data.reset_index(drop=False)) 1. 2. 3 以某列值设为新的索引 set_index(keys,drop=True) keys:列索引名称或者列索引名称的列表 drop:默认为True,当作新的索引,删除原来索引 # 创建 df = pd.DataFrame({ 'month': [1, 4, 7, 10], 'year': [2018, 2019, 2020, 2021], '...