print(df)读取内嵌JSON中的一组数据使用glom 模块来处理数据套嵌,glom 模块允许我们使用 . 来访问内嵌对象的属性安装glom pip3 install glom 使用 import pandas as pd from glom import glom df = pd.read_json('nested_deep.json') data = df['students'].apply(lambda row: glom(row, 'grade.math'))...
In [1]: from numba import jit, njit, vectorize, float64 In [2]: def custom_mean(x): return (x * x).mean() In [3]: @jit(cache=True) def custom_mean_jitted(x): return (x * x).mean() In [4]: %timeit rolling_df.apply(custom_mean, raw=True) CPU times: user 4.33 s, ...
Importing namedtuple from collectionsfromcollectionsimportnamedtuple# Creating a namedtuplePoint=namedtuple('Point', ['x','y'])# Assiging tuples some valuespoints=[Point(1,2), Point(3,4)]# Creating DataFramedf=pd.DataFrame(points)# Display original DataFrameprint('Original DataFrame:\n',df,'\...
If the tuple contains nested tuples or lists, each nested tuple/list becomes a row in the DataFrame. import pandas as pd list_of_tuples = [(1, 2, 3), (4, 5, 6), (7, 8, 9)] print("Before converting to data frame") print(list_of_tuples) df = pd.DataFrame(list_of_tuple...
可以从结构化数组中创建DF: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 In [47]: data = np.zeros((2, ), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10')]) In [48]: data[:] = [(1, 2., 'Hello'), (2, 3., "World")] In [49]: pd.DataFrame(data) Out[49...
df = pd.DataFrame.from_dict(sales) Both of these approaches will give you the results in the order you would likely expect. For reasons I outline below, I tend to specifically re-order my columns vs. using an OrderedDict but it is always good to understand the options. ...
df我们可以看到,这里的行索引为 a, b, c, d ,而列索引为 one, two。4.2 ndarrays 或 lists 字典 -> DataFrame# 列表构成的字典 d = {'one' : [1, 2, 3, 4], 'two' : [4, 3, 2, 1]} df1 = pd.DataFrame(d) # 未指定索引 df2 = pd.DataFrame(d, index=['a', 'b', 'c', '...
0 NaN False列可以像字典一样删除或弹出：del df["two"] three = df.pop("three") df ...
fromdatetimeimportdatecreate_date="{:%m-%d-%Y}".format(date.today())created_by="CM"footer=[('Created by',[created_by]),('Created on',[create_date]),('Version',[1.1])]df_footer=pd.DataFrame.from_items(footer) Combine into a single Excel sheet: ...
请注意,在这个例子中,df.loc['bar', 'two']也可以工作,但这种简写符号通常会导致歧义。 如果你还想使用.loc索引特定列,你必须像这样使用元组: 代码语言:javascript 代码运行次数:0 运行 复制 In [42]: df.loc[("bar", "two"), "A"] Out[42]: 0.8052440253863785 你不必通过仅传递元组的第一个元素来...