'Princi','Gaurav','Anuj'],'Age':[27,24,22,32],'Address':['Delhi','Kanpur','Allahabad','Kannauj'],'Qualification':['Msc','MA','MCA','Phd']}# Convert the dictionary into DataFramedf=pd.DataFrame(data)# select two columnsdf[['Name','Qualification']]...
# 对索引名进行修改s.rename_axis("animal")df.rename_axis("animal") # 默认是列索引df.rename_axis("limbs",axis="columns") # 指定行索引 # 索引为多层索引时可以将type修改为classdf.rename_axis(index={'type': 'class'}) # 可以用set_axis进行设置修改s.set...
单个df按条件配号 importnumpy as npconditions= [c1,c2,c3,c4,c5,c6] #其中,c1-c6是布尔表达式values= [1,2,3,4,5,6]df[column] = np.select(conditions, values)
(self) 1489 ref = self._get_cacher() 1490 if ref is not None and ref._is_mixed_type: 1491 self._check_setitem_copy(t="referent", force=True) 1492 return True -> 1493 return super()._check_is_chained_assignment_possible() ~/work/pandas/pandas/pandas/core/generic.py in ?(self) ...
columns: print(type(df[col])) print(df[col]) #代码运行结果: <class 'pandas.core.series.Series'> 0 1 1 4 2 7 Name: a, dtype: int64 … 两者的数据结构差别如表所示 名称 维度 描述 Series 1 带标签的一维同构数组 DataFrame 2 带标签的大小可变的二维异构表格 Pandas 所有数据结构的值都是...
axis="columns") # 指定行索引 # 索引为多层索引时可以将type修改为class df.rename_axis(index={'type': 'class'}) # 可以用set_axis进行设置修改 s.set_axis(['a', 'b', 'c'], axis=0) df.set_axis(['I', 'II'], axis='columns') df.set_axis(['i', 'ii'], axis='columns',in...
pymysql.connect(host='127.0.0.1', port=3306, user='root', passwd='123456', db='test')5#engine = create_engine('mysql+pymysql://root:123456@localhost:3306/test')6sql_str ='select * from employee'7result =pd.io.sql.read_sql_query(sql_str, conn)8print(type(result),'\n', result...
columns=shorten)>>>movies.get_dtype_counts()float6413int643object12dtype:int64（2）使用.select_...
read_excel可以通过将列列表传递给index_col和将行列表传递给header来读取MultiIndex索引。如果index或columns具有序列化级别名称,也可以通过指定构成级别的行/列来读取这些级别。 例如,要读取没有名称的MultiIndex索引: In [424]: df = pd.DataFrame(...: {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]...
pandas.read_sql(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None) import pymysql con =pymysql.connect( host=‘localhost’,user=‘root’,password=‘root’,database=‘test’,port=3306,charset=‘utf8’) sql_select = ‘select * from...