In [21]: sa.a = 5 In [22]: sa Out[22]: a 5 b 2 c 3 dtype: int64 In [23]: dfa.A = list(range(len(dfa.index))) # ok if A already exists In [24]: dfa Out[24]: A B C D 2000-01-01 0 0.469112 -1.509059 -1.135632 2000-01-02 1 1.212112 0.119209 -1.044236 2000-01...
import pandas as pd # 使用字典创建 DataFrame 并指定列名作为索引 mydata = {'Column1': [1, 2, 3], 'Column2': ['a', 'b', 'c']} df = pd.DataFrame(mydata) df # 输出 Column1 Column2 0 1 a 1 2 b 2 3 c 指定行索引: # 指定行索引 df.index = ['row1', 'row2', '...
21, 62], ['Scot', 25, 68]], index=[0, 1, 2], columns=['Name', 'Ag...
index=["first", "second"]) Out[55]: a b c first 1 2 NaN second 5 10 20.0 In [56]: pd.DataFrame(data2, columns=["a", "b"]) Out[56]: a b 0 1 2 1 5
In[47]: pd.set_option("large_repr", "info")In[48]: dfOut[48]:<class'pandas.core.frame.DataFrame'>RangeIndex:10entries,0to9Data columns (total10columns): #ColumnNon-NullCount Dtype--- --- --- ---0010non-nullfloat641110non-nullfloat642210non-nullfloat643310non-nullfloat644410non...
s = pd.Series(data, index=index) 在这里,data可以是许多不同的东西: 一个Python 字典 一个ndarray 标量值(比如 5) 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, ..., ...
# importing pandas packageimportpandasaspd# making data frame from csv filedata = pd.read_csv("employees.csv")# setting first name as index columndata.set_index("First Name", inplace =True)# displaydata.head() 输出: 如输出图像中所示,索引列之前是一系列数字,但后来已被名字替换。
import pandas as pd # 导入 NumPy 库 import numpy as np # 通过列表数据创建 # columns: 列数据标签 # index: 行数据标签 s_data = pd.DataFrame([[5.1,3.5,1.4,0.2], [6.1,3.7,4.1,1.5], [5.8,2.7,5.1,1.9]], columns=['feature_one','feature_two','feature_three','feature_four'], index...
the sorted keys will be used as the `keys`argument, unless it is passed, in which case the values will beselected (see below). Any None objects will be dropped silently unlessthey are all None in which case a ValueError will be raised.axis : {0/'index', 1/'columns'}, default 0The...
importpandasaspdimportnumpyasnp# 创建示例多层索引DataFramearrays = [np.array(['bar','bar','baz','baz']), np.array(['one','two','one','two'])] index = pd.MultiIndex.from_arrays(arrays, names=['first','second']) df = pd.DataFrame(np.random.randn(4,2), index=index, columns=[...