113 lines
4.2 KiB
Python
113 lines
4.2 KiB
Python
# -*- encoding:utf-8 -*-
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"""直观可视化制作qcut的bins点"""
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from __future__ import division
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from __future__ import print_function
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from __future__ import absolute_import
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import math
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import logging
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import itertools
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# noinspection PyUnresolvedReferences
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from ..CoreBu.ABuFixes import filter
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__all__ = ['show_orders_hist']
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def show_orders_hist(df, feature_columns, show=True, only_hist=True, show_pie=False):
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"""
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可视化统计feature_columns序列所指定的特征在df中的直方图或者饼状图，
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根据参数only_hist是否进行pd.qcut统计输出
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eg:
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from abupy import AbuML, ml
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ttn_raw = AbuML.load_ttn_raw_df()
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ml.show_orders_hist(ttn_raw, ['Age', 'Fare', 'Pclass'])
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:param df: pd.DataFrame对象
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:param feature_columns: 特征名称序列，eg：['Age', 'Fare', 'Pclass']
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:param show: 是否可视化直方图或者饼状图
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:param show_pie: 是否优先考虑绘制饼状图，默认false
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:param only_hist: 是否进行pd.qcut统计输出
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"""
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if not isinstance(df, pd.DataFrame):
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logging.info('df must pd.DataFrame, not type {}'.format(type(df)))
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return
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# 第一步过滤不在在特征列中的feature_columns元素
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feature_columns = list(filter(lambda x: df.columns.tolist().count(x) > 0, feature_columns))
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# 第二步过滤feature_columns元素中类型不是int或者float的
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feature_columns = list(
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filter(
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lambda x: df[x].dtype == int or df[x].dtype == float or df[x].dtype == np.uint or df[x].dtype == np.uint8,
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feature_columns))
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# 第三步过滤feature_columns元素中所指特征列中unique==1的，eg：1列全是1，全是0，没办法做bin
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feature_columns = list(filter(lambda x: len(np.unique(df[x])) > 1, feature_columns))
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axs_list = None
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if len(feature_columns) == 0:
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# 晒没了的情况，直接返回
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logging.info('{}\n{}\nnot exist! or unique==1!, or dtype != int or float'.format(
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df.columns, df.dtypes))
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return
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if show:
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# 如果可视化直方图，先确定子画布列数，一行放两个，取math.ceil，eg：3 ／2 ＝ 2
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n_rows = int(math.ceil(len(feature_columns) / 2))
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# 行高取5，总高度：n_rows * 5
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fig_h = n_rows * 5
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# plt.subplots生成子画布
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_, axs = plt.subplots(nrows=n_rows, ncols=2, figsize=(14, fig_h))
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# 如果是多于1个的即展开字画本序列为1d序列
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axs_list = axs if n_rows == 1 else list(itertools.chain.from_iterable(axs))
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for ind, feature in enumerate(feature_columns):
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feature_unique = len(np.unique(df[feature]))
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ax = None
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if axs_list is not None:
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ax = axs_list[ind]
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ax.set_title(feature)
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if show_pie and feature_unique < 10:
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# 如果特征的值unique < 10个，通过value_counts直接画饼图
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df[feature].value_counts().plot(ax=ax, kind='pie')
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else:
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# 画直方图
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bins = int(feature_unique / 50) if feature_unique / 50 > 10 else 10
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df[feature].hist(ax=ax, bins=bins)
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if only_hist:
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# 只做可视化就continue
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continue
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try:
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# qcut切分10等份
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cats = pd.qcut(df[feature], 10)
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except Exception:
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# 某一个数据超出q的数量导致无法分
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import pandas.core.algorithms as algos
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bins = algos.quantile(np.unique(df[feature]), np.linspace(0, 1, 10 + 1))
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# noinspection PyProtectedMember,PyUnresolvedReferences
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cats = pd.tools.tile._bins_to_cuts(df[feature], bins, include_lowest=True)
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logging.info('{0} show hist and qcuts'.format(feature))
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"""
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Age show hist and qcuts
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(31.8, 36] 91
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(14, 19] 87
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(41, 50] 78
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[0.42, 14] 77
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(22, 25] 70
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(19, 22] 67
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(28, 31.8] 66
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(50, 80] 64
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(25, 28] 61
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(36, 41] 53
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Name: Age, dtype: int64
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"""
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logging.info(cats.value_counts())
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