每日一悟

【分开工作内外8小时】前一个月，我经常把工作内的问题带到路上、地铁上、睡觉前，甚至是周末。然而很快发现，我工作外的成就几乎没有，而工作内的进展也并不理想。仔细想想，工作外是需要学新东西，产生新灵感。一方面是工作内的支撑，另一方面也是新的方向。而不是低效率地光在脑子里想工作内的解决方案。所以，我觉得有必要明确工作内外的目标和行动，比如工作外每周一本书，每天的原版技术书阅读；工作内做好事务优先级，处理前先想清楚思路再着手准备。高效且多产，这才是目的。

pandas.pivot_table

pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All')

简介：

method of pandas.core.frame.DataFrame instance Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

pandas核心实例的方法，创建一个大宽表的透视表数据框，在这个结果数据框中的索引和列等级，将会被存储在多重索引对象中（分层索引）。

应用格式：

pandas.pivot_table(dataframe,Other parameters)

等同于

dataframe.pivot_table(Other parameters)

参数：

在看参数之前我们先看看Excel中透视表的结构，结构为筛选、列、行、值。除了筛选，列、行、值与下面要介绍的pandas.pivot_table功能一值。

data : 要应用透视表的数据框；

values: 可选，是要聚合的列，相当于“值”，例如 values=["Price"]；

index : 是要聚合值的分组，相当于“行”，多个层次格式例如 index=["Name","Rep","Manager"]；

columns : 是要聚合值的分组，相当于“列”；

aggfunc : 是要应用的聚合函数，指定不同值使用不同聚合函数时可用字典格式，例如 aggfunc=[np.mean,len]，aggfunc={"Quantity":len,"Price":[np.sum,np.mean]}；

fill_value : 有时候聚合结果里出现了NaN，想替换成0时，fill_value=0；

margins : 是否添加所有行或列的小计/总计，margins=True；

margins_name : 当margins设置为True时，设置总计的名称，默认是“ALL”。

举例：

见help(pandas.pivot_table)

pandas.crosstab

crosstab(index, columns, values=None, rownames=None, colnames=None, aggfunc=None, margins=False,margins_name='All', dropna=True, normalize=False)

Compute a simple cross-tabulation of two (or more) factors. By default    computes a frequency table of the factors unless an array of values and an    aggregation function are passed        Parameters    ----------    index : array-like, Series, or list of arrays/Series        Values to group by in the rows    columns : array-like, Series, or list of arrays/Series        Values to group by in the columns    values : array-like, optional        Array of values to aggregate according to the factors.        Requires `aggfunc` be specified.    aggfunc : function, optional        If specified, requires `values` be specified as well    rownames : sequence, default None        If passed, must match number of row arrays passed    colnames : sequence, default None        If passed, must match number of column arrays passed    margins : boolean, default False        Add row/column margins (subtotals)    margins_name : string, default 'All'        Name of the row / column that will contain the totals        when margins is True.            .. versionadded:: 0.21.0        dropna : boolean, default True        Do not include columns whose entries are all NaN    normalize : boolean, {'all', 'index', 'columns'}, or {0,1}, default False        Normalize by dividing all values by the sum of values.            - If passed 'all' or `True`, will normalize over all values.        - If passed 'index' will normalize over each row.        - If passed 'columns' will normalize over each column.        - If margins is `True`, will also normalize margin values.            .. versionadded:: 0.18.1            Notes    -----    Any Series passed will have their name attributes used unless row or column    names for the cross-tabulation are specified.        Any input passed containing Categorical data will have **all** of its    categories included in the cross-tabulation, even if the actual data does    not contain any instances of a particular category.        In the event that there aren't overlapping indexes an empty DataFrame will    be returned.        Examples    --------a = np.array(["foo", "foo", "foo", "foo", "bar", "bar",              "bar", "bar", "foo", "foo", "foo"], dtype=object)b = np.array(["one", "one", "one", "two", "one", "one",              "one", "two", "two", "two", "one"], dtype=object)c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny",              "shiny", "dull", "shiny", "shiny", "shiny"],              dtype=object)    pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])# doctest: +NORMALIZE_WHITESPACE    b   one        two    c   dull shiny dull shiny    a    bar    1     2    1     0    foo    2     2    1     2    foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])crosstab(foo, bar)  # 'c' and 'f' are not represented in the data,                    # but they still will be counted in the output# doctest: +SKIP    col_0  d  e  f    row_0    a      1  0  0    b      0  1  0    c      0  0  0        Returns    -------    crosstab : DataFrame