In [45]: s1 = pd.Series(np.random.randn(6), index=list('abcdef')) In [46]: s1 Out[46]: a 1.431256 b 1.340309 c -1.170299 d -0.226169 e 0.410835 f 0.813850 dtype: float64 In [47]: s1.loc['c':] Out[47]: c -1.170299 d -0.226169 e 0.410835 f 0.813850 dtype: float64 In...
通过使用@jit修饰的自定义 Python 函数,可以通过使用Series.to_numpy()将它们的 NumPy 数组表示传递给 pandas 对象。 代码语言:javascript 复制 import numba @numba.jit def f_plain(x): return x * (x - 1) @numba.jit def integrate_f_numba(a, b, N): s = 0 dx = (b - a) / N for i i...
ncalls tottime percall cumtime percall filename:lineno(function)10000.0970.0000.1480.000<ipython-input-4-c2a74e076cf0>:1(integrate_f)5524230.0510.0000.0510.000<ipython-input-3-c138bdd570e3>:1(f)30000.0030.0000.0120.000series.py:1095(__getitem__)30000.0020.0000.0050.000series.py:1220(_get_val...
File ~/work/pandas/pandas/pandas/core/series.py:5153,inSeries.reindex(self, index, axis, method, copy, level, fill_value, limit, tolerance)5136@doc(5137NDFrame.reindex,# type: ignore[has-type]5138klass=_shared_doc_kwargs["klass"], (...)5151tolerance=None,5152) -> Series: ->5153retur...
在分类数据上使用describe()将产生类似于string类型的Series或DataFrame的输出。 In [53]: cat = pd.Categorical(["a", "c", "c", np.nan], categories=["b", "a", "c"])In [54]: df = pd.DataFrame({"cat": cat, "s": ["a", "c", "c", np.nan]})In [55]: df.describe()Out...
如果切片操作返回DataFrame或Series类型的列,则categorydtype 会被保留。 In [144]: idx = pd.Index(["h", "i", "j", "k", "l", "m", "n"])In [145]: cats = pd.Series(["a", "b", "b", "b", "c", "c", "c"], dtype="category", index=idx)In [146]: values = [1, ...
from pandas import Series ''' Series的方法与属性 属性: values 获取某一列的数据值 获取的值为numpy.ndarray类型 index 获取series数据 方法: Series(数值项,index=索引的项) #数值项 与索引的项必须一一对应 ,索引项可以为字符串 index.tolist() names ...
string[python] 1. 在创建Series的时候可以直接指定数据类型: s2 = pd.Series(['a','b','c',None], dtype='string') s2 1. 2. 0 a 1 b 2 c 3 <NA> dtype: string 1. 2. 3. 4. 5. s2.dtype 1. string[python] 1. convert_dtypes转化数据类型 ...
Now that you’ve got the basics down, let’s explore some more advanced uses of thereset_index()function. These techniques can help you manipulate your data in more complex ways. Dropping the Old Index Sometimes, you might want to reset the index without keeping the old index however the ...
()) # The function for a pandas_udf should be able to execute with local pandas data x = pd.Series([1, 2, 3]) print(multiply_func(x, x)) # 0 1 # 1 4 # 2 9 # dtype: int64 # Create a Spark DataFrame, 'spark' is an existing SparkSession df = spark.createDataFrame(pd....