In [35]: columns = pd.MultiIndex.from_tuples( ...: [ ...: ("A", "cat", "long"), ...: ("B", "cat", "long"), ...: ("A", "dog", "short"), ...: ("B", "dog", "short"), ...: ], ...: names=["exp", "animal", "hair_length"], ...: ) ...: In ...
In [31]: df[["foo", "qux"]].columns.to_numpy() Out[31]: array([('foo', 'one'), ('foo', 'two'), ('qux', 'one'), ('qux', 'two')], dtype=object) # for a specific level In [32]: df[["foo", "qux"]].columns.get_level_values(0) Out[32]: Index(['foo', 'f...
通过拦截 Pandas API 调用并将其映射到 cuDF 的 GPU 实现来加速现有代码。
默认读取第一个 sheetstudent = pd.read_excel(r'C:\Users\Administrator\Desktop\Temp\1.xlsx')# 2.读取常用属性print(student.shape) # 形状(行,列)print(student.columns) # 列名 读取指定 sheet: import pandas as pd# 1.读取指定 sheet 的 excel,以下两种方式等同student = pd.read_excel(r'C:\User...
f= codecs.open(filePath,'w','utf-8') f.write(cont) f.flush() f.close() 参考链接:http://stackoverflow.com/questions/15125343/how-to-iterate-through-two-pandas-columns 生活不易,本人有意向做数据分析兼职或python在线辅导,如有需要请联系qq号1334832194。
columns = df.columns.str.replace(' ', '_') # 列名空格换下划线 df.loc[df.AAA >= 5, ['BBB', 'CCC']] = 555 # 替换数据 df['pf'] = df.site_id.map({2: '小程序', 7:'M 站'}) # 将枚举换成名称 pd.isnull() # 检查DataFrame对象中的空值,并返回一个 Boolean 数组 pd.notnull...
[1rows x13columns] (对她的故事感兴趣吗?请参阅维基百科!) 字符串方法Series.str.contains()检查列Name中的每个值是否包含单词Countess,并对每个值返回True(Countess是名称的一部分)或False(Countess不是名称的一部分)。此输出可用于使用在数据子集教程中介绍的条件(布尔)索引来对数据进行子选择。由于泰坦尼克号上...
# Importing pandas packageimportpandasaspd# Creating two list of tuplesdata=[ ('Ram','APPLE',23), ('Shyam','GOOGLE',25), ('Seeta','GOOGLE',22), ('Geeta','MICROSOFT',24), ('Raman','GOOGLE',23), ('Sahil','SAMSUNG',23) ]# Creating a DataFramedf=pd.DataFrame(data,columns=['Na...
fromtypingimportIterator, Tupleimportpandasaspdfrompyspark.sql.functionsimportcol, pandas_udf, struct pdf = pd.DataFrame([1,2,3], columns=["x"]) df = spark.createDataFrame(pdf)@pandas_udf("long")defmultiply_two_cols( iterator: Iterator[Tuple[pd.Series, pd.Series]])-> Iterator[pd.Series...
One_X One_Y Two_X Two_Y row 0 1.1 1.2 1.11 1.22 1 1.1 1.2 1.11 1.22 2 1.1 1.2 1.11 1.22 # 多层索引的列 In [68]: df.columns = pd.MultiIndex.from_tuples([tuple(c.split('_')) ...: for c in df.columns]) ...: In ...