Pandas 精简实例入门
Posted hp-lake
tags:
篇首语：本文由小常识网(cha138.com)小编为大家整理，主要介绍了Pandas 精简实例入门相关的知识，希望对你有一定的参考价值。
import pandas as pd
import numpy as np
0. 案例引入
# 由np直接生成的ndarray
stock_change = np.random.normal(0, 1, (10, 8))
stock_change
array([[ 0.74057955, 0.78604657, -0.15264135, 0.05680483, 0.09388135,
0.7313751 , -1.52338443, 1.71156505],
[ 0.42204925, 0.62541715, -1.41583042, -0.27434654, 0.98587136,
-0.55797884, 0.31026482, -0.47964535],
[ 0.99741102, -0.94397298, -0.40782973, -1.33631227, -0.0124836 ,
1.1873408 , -0.25430393, -0.74264106],
[ 0.34156662, -0.40621262, 0.82861416, 0.1272128 , 1.04101412,
0.79061324, -0.60325544, 1.29954581],
[-1.23289547, 0.83789748, 1.19276989, 0.45092868, -1.7418129 ,
-0.65362211, -0.17752493, 1.87679286],
[-0.4268705 , 1.14017572, 0.18261009, -0.28947877, 0.82489897,
0.11566058, -0.53191371, -0.96065812],
[ 0.92792797, 0.26086313, 0.08316582, -0.94533007, -0.77956139,
0.23006703, -0.81971461, -1.36742474],
[ 0.82241768, 0.54201367, -0.19331564, 0.50576697, -0.42545839,
-0.24247517, -0.03526651, -0.02268451],
[ 1.67480093, -1.23265948, -2.88199942, -1.07761987, -1.37844497,
-0.13581683, 2.06013919, 1.18986057],
[ 0.60744357, 0.52348326, 0.76418263, -0.73385554, 0.54857341,
0.27310645, -0.26464179, 0.77370496]])
# 通过pd.DataFrame生成 (pd.DataFrame(ndarray))
stock_df = pd.DataFrame(stock_change)
stock_df
|
0 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
| 0 |
0.740580 |
0.786047 |
-0.152641 |
0.056805 |
0.093881 |
0.731375 |
-1.523384 |
1.711565 |
| 1 |
0.422049 |
0.625417 |
-1.415830 |
-0.274347 |
0.985871 |
-0.557979 |
0.310265 |
-0.479645 |
| 2 |
0.997411 |
-0.943973 |
-0.407830 |
-1.336312 |
-0.012484 |
1.187341 |
-0.254304 |
-0.742641 |
| 3 |
0.341567 |
-0.406213 |
0.828614 |
0.127213 |
1.041014 |
0.790613 |
-0.603255 |
1.299546 |
| 4 |
-1.232895 |
0.837897 |
1.192770 |
0.450929 |
-1.741813 |
-0.653622 |
-0.177525 |
1.876793 |
| 5 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 6 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 7 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 8 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 9 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
stock_df.shape
(10, 8)
# 添加行索引
stock_name = [‘股票{}‘.format(i+1) for i in range(stock_df.shape[0])]
stock_df = pd.DataFrame(data=stock_change, index=stock_name)
stock_df
|
0 |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
| 股票1 |
0.740580 |
0.786047 |
-0.152641 |
0.056805 |
0.093881 |
0.731375 |
-1.523384 |
1.711565 |
| 股票2 |
0.422049 |
0.625417 |
-1.415830 |
-0.274347 |
0.985871 |
-0.557979 |
0.310265 |
-0.479645 |
| 股票3 |
0.997411 |
-0.943973 |
-0.407830 |
-1.336312 |
-0.012484 |
1.187341 |
-0.254304 |
-0.742641 |
| 股票4 |
0.341567 |
-0.406213 |
0.828614 |
0.127213 |
1.041014 |
0.790613 |
-0.603255 |
1.299546 |
| 股票5 |
-1.232895 |
0.837897 |
1.192770 |
0.450929 |
-1.741813 |
-0.653622 |
-0.177525 |
1.876793 |
| 股票6 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 股票7 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 股票8 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 股票9 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 股票10 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
# 添加列索引
# 引入df.date_range(),start-开始日期, end: 结束日期, periods - 持续时间, frep- B:工作日, M:月, D:天
date = pd.date_range(start=‘2020-3-30‘, periods=stock_df.shape[1], freq=‘d‘)
date
DatetimeIndex([‘2020-03-30‘, ‘2020-03-31‘, ‘2020-04-01‘, ‘2020-04-02‘,
‘2020-04-03‘, ‘2020-04-04‘, ‘2020-04-05‘, ‘2020-04-06‘],
dtype=‘datetime64[ns]‘, freq=‘D‘)
stock_df = pd.DataFrame(stock_change, index=stock_name, columns=date)
stock_df
|
2020-03-30 |
2020-03-31 |
2020-04-01 |
2020-04-02 |
2020-04-03 |
2020-04-04 |
2020-04-05 |
2020-04-06 |
| 股票1 |
0.740580 |
0.786047 |
-0.152641 |
0.056805 |
0.093881 |
0.731375 |
-1.523384 |
1.711565 |
| 股票2 |
0.422049 |
0.625417 |
-1.415830 |
-0.274347 |
0.985871 |
-0.557979 |
0.310265 |
-0.479645 |
| 股票3 |
0.997411 |
-0.943973 |
-0.407830 |
-1.336312 |
-0.012484 |
1.187341 |
-0.254304 |
-0.742641 |
| 股票4 |
0.341567 |
-0.406213 |
0.828614 |
0.127213 |
1.041014 |
0.790613 |
-0.603255 |
1.299546 |
| 股票5 |
-1.232895 |
0.837897 |
1.192770 |
0.450929 |
-1.741813 |
-0.653622 |
-0.177525 |
1.876793 |
| 股票6 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 股票7 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 股票8 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 股票9 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 股票10 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
1. Pandas 主要数据结构
1.1 DataFrame
stock_df
|
2020-03-30 |
2020-03-31 |
2020-04-01 |
2020-04-02 |
2020-04-03 |
2020-04-04 |
2020-04-05 |
2020-04-06 |
| 股票1 |
0.740580 |
0.786047 |
-0.152641 |
0.056805 |
0.093881 |
0.731375 |
-1.523384 |
1.711565 |
| 股票2 |
0.422049 |
0.625417 |
-1.415830 |
-0.274347 |
0.985871 |
-0.557979 |
0.310265 |
-0.479645 |
| 股票3 |
0.997411 |
-0.943973 |
-0.407830 |
-1.336312 |
-0.012484 |
1.187341 |
-0.254304 |
-0.742641 |
| 股票4 |
0.341567 |
-0.406213 |
0.828614 |
0.127213 |
1.041014 |
0.790613 |
-0.603255 |
1.299546 |
| 股票5 |
-1.232895 |
0.837897 |
1.192770 |
0.450929 |
-1.741813 |
-0.653622 |
-0.177525 |
1.876793 |
| 股票6 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 股票7 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 股票8 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 股票9 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 股票10 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
# 查看DataFrame形状,类似于2d array
stock_df.shape
(10, 8)
# 取行索引
stock_df.index
Index([‘股票1‘, ‘股票2‘, ‘股票3‘, ‘股票4‘, ‘股票5‘, ‘股票6‘, ‘股票7‘, ‘股票8‘, ‘股票9‘, ‘股票10‘], dtype=‘object‘)
# 取列索引
stock_df.columns
DatetimeIndex([‘2020-03-30‘, ‘2020-03-31‘, ‘2020-04-01‘, ‘2020-04-02‘,
‘2020-04-03‘, ‘2020-04-04‘, ‘2020-04-05‘, ‘2020-04-06‘],
dtype=‘datetime64[ns]‘, freq=‘D‘)
# 取ndarray的值
stock_df.values
array([[ 0.74057955, 0.78604657, -0.15264135, 0.05680483, 0.09388135,
0.7313751 , -1.52338443, 1.71156505],
[ 0.42204925, 0.62541715, -1.41583042, -0.27434654, 0.98587136,
-0.55797884, 0.31026482, -0.47964535],
[ 0.99741102, -0.94397298, -0.40782973, -1.33631227, -0.0124836 ,
1.1873408 , -0.25430393, -0.74264106],
[ 0.34156662, -0.40621262, 0.82861416, 0.1272128 , 1.04101412,
0.79061324, -0.60325544, 1.29954581],
[-1.23289547, 0.83789748, 1.19276989, 0.45092868, -1.7418129 ,
-0.65362211, -0.17752493, 1.87679286],
[-0.4268705 , 1.14017572, 0.18261009, -0.28947877, 0.82489897,
0.11566058, -0.53191371, -0.96065812],
[ 0.92792797, 0.26086313, 0.08316582, -0.94533007, -0.77956139,
0.23006703, -0.81971461, -1.36742474],
[ 0.82241768, 0.54201367, -0.19331564, 0.50576697, -0.42545839,
-0.24247517, -0.03526651, -0.02268451],
[ 1.67480093, -1.23265948, -2.88199942, -1.07761987, -1.37844497,
-0.13581683, 2.06013919, 1.18986057],
[ 0.60744357, 0.52348326, 0.76418263, -0.73385554, 0.54857341,
0.27310645, -0.26464179, 0.77370496]])
# 取转置
stock_df.T
|
股票1 |
股票2 |
股票3 |
股票4 |
股票5 |
股票6 |
股票7 |
股票8 |
股票9 |
股票10 |
| 2020-03-30 |
0.740580 |
0.422049 |
0.997411 |
0.341567 |
-1.232895 |
-0.426871 |
0.927928 |
0.822418 |
1.674801 |
0.607444 |
| 2020-03-31 |
0.786047 |
0.625417 |
-0.943973 |
-0.406213 |
0.837897 |
1.140176 |
0.260863 |
0.542014 |
-1.232659 |
0.523483 |
| 2020-04-01 |
-0.152641 |
-1.415830 |
-0.407830 |
0.828614 |
1.192770 |
0.182610 |
0.083166 |
-0.193316 |
-2.881999 |
0.764183 |
| 2020-04-02 |
0.056805 |
-0.274347 |
-1.336312 |
0.127213 |
0.450929 |
-0.289479 |
-0.945330 |
0.505767 |
-1.077620 |
-0.733856 |
| 2020-04-03 |
0.093881 |
0.985871 |
-0.012484 |
1.041014 |
-1.741813 |
0.824899 |
-0.779561 |
-0.425458 |
-1.378445 |
0.548573 |
| 2020-04-04 |
0.731375 |
-0.557979 |
1.187341 |
0.790613 |
-0.653622 |
0.115661 |
0.230067 |
-0.242475 |
-0.135817 |
0.273106 |
| 2020-04-05 |
-1.523384 |
0.310265 |
-0.254304 |
-0.603255 |
-0.177525 |
-0.531914 |
-0.819715 |
-0.035267 |
2.060139 |
-0.264642 |
| 2020-04-06 |
1.711565 |
-0.479645 |
-0.742641 |
1.299546 |
1.876793 |
-0.960658 |
-1.367425 |
-0.022685 |
1.189861 |
0.773705 |
# 查看头部几行数据, 默认5行
stock_df.head(5)
# 查看倒数几行数据
stock_df.tail()
|
2020-03-30 |
2020-03-31 |
2020-04-01 |
2020-04-02 |
2020-04-03 |
2020-04-04 |
2020-04-05 |
2020-04-06 |
| 股票6 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 股票7 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 股票8 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 股票9 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 股票10 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
1.1.1 设置索引
# 只能通过对整个index 重新赋值, 整行或者整列
data_index = [[‘股票__{}‘.format(i+1) for i in range(stock_df.shape[0])]]
stock_df.index = data_index
stock_df
|
2020-03-30 |
2020-03-31 |
2020-04-01 |
2020-04-02 |
2020-04-03 |
2020-04-04 |
2020-04-05 |
2020-04-06 |
| 股票__1 |
0.740580 |
0.786047 |
-0.152641 |
0.056805 |
0.093881 |
0.731375 |
-1.523384 |
1.711565 |
| 股票__2 |
0.422049 |
0.625417 |
-1.415830 |
-0.274347 |
0.985871 |
-0.557979 |
0.310265 |
-0.479645 |
| 股票__3 |
0.997411 |
-0.943973 |
-0.407830 |
-1.336312 |
-0.012484 |
1.187341 |
-0.254304 |
-0.742641 |
| 股票__4 |
0.341567 |
-0.406213 |
0.828614 |
0.127213 |
1.041014 |
0.790613 |
-0.603255 |
1.299546 |
| 股票__5 |
-1.232895 |
0.837897 |
1.192770 |
0.450929 |
-1.741813 |
-0.653622 |
-0.177525 |
1.876793 |
| 股票__6 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 股票__7 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 股票__8 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 股票__9 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 股票__10 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
# stock_df.index[3] =‘hahha‘
# stock_df
1.1.2 重设索引
# reset_index在原来基础上新增一列索引
# drop=False(默认) - 不丢弃原来索引
# drop=True - 丢掉原来索引 index
stock_df.reset_index()
|
level_0 |
2020-03-30 00:00:00 |
2020-03-31 00:00:00 |
2020-04-01 00:00:00 |
2020-04-02 00:00:00 |
2020-04-03 00:00:00 |
2020-04-04 00:00:00 |
2020-04-05 00:00:00 |
2020-04-06 00:00:00 |
| 0 |
股票__1 |
0.740580 |
0.786047 |
-0.152641 |
0.056805 |
0.093881 |
0.731375 |
-1.523384 |
1.711565 |
| 1 |
股票__2 |
0.422049 |
0.625417 |
-1.415830 |
-0.274347 |
0.985871 |
-0.557979 |
0.310265 |
-0.479645 |
| 2 |
股票__3 |
0.997411 |
-0.943973 |
-0.407830 |
-1.336312 |
-0.012484 |
1.187341 |
-0.254304 |
-0.742641 |
| 3 |
股票__4 |
0.341567 |
-0.406213 |
0.828614 |
0.127213 |
1.041014 |
0.790613 |
-0.603255 |
1.299546 |
| 4 |
股票__5 |
-1.232895 |
0.837897 |
1.192770 |
0.450929 |
-1.741813 |
-0.653622 |
-0.177525 |
1.876793 |
| 5 |
股票__6 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 6 |
股票__7 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 7 |
股票__8 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 8 |
股票__9 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 9 |
股票__10 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
stock_df.reset_index(drop=True)
|
2020-03-30 |
2020-03-31 |
2020-04-01 |
2020-04-02 |
2020-04-03 |
2020-04-04 |
2020-04-05 |
2020-04-06 |
| 0 |
0.740580 |
0.786047 |
-0.152641 |
0.056805 |
0.093881 |
0.731375 |
-1.523384 |
1.711565 |
| 1 |
0.422049 |
0.625417 |
-1.415830 |
-0.274347 |
0.985871 |
-0.557979 |
0.310265 |
-0.479645 |
| 2 |
0.997411 |
-0.943973 |
-0.407830 |
-1.336312 |
-0.012484 |
1.187341 |
-0.254304 |
-0.742641 |
| 3 |
0.341567 |
-0.406213 |
0.828614 |
0.127213 |
1.041014 |
0.790613 |
-0.603255 |
1.299546 |
| 4 |
-1.232895 |
0.837897 |
1.192770 |
0.450929 |
-1.741813 |
-0.653622 |
-0.177525 |
1.876793 |
| 5 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 6 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 7 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 8 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 9 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
1.1.3 以某列为索引
stock_df.set_index(keys=‘2020-03-30‘, drop=False) #此处因为类型问题,都是drop 原来的index
|
2020-03-30 |
2020-03-31 |
2020-04-01 |
2020-04-02 |
2020-04-03 |
2020-04-04 |
2020-04-05 |
2020-04-06 |
| 2020-03-30 |
|
|
|
|
|
|
|
|
| 0.740580 |
0.740580 |
0.786047 |
-0.152641 |
0.056805 |
0.093881 |
0.731375 |
-1.523384 |
1.711565 |
| 0.422049 |
0.422049 |
0.625417 |
-1.415830 |
-0.274347 |
0.985871 |
-0.557979 |
0.310265 |
-0.479645 |
| 0.997411 |
0.997411 |
-0.943973 |
-0.407830 |
-1.336312 |
-0.012484 |
1.187341 |
-0.254304 |
-0.742641 |
| 0.341567 |
0.341567 |
-0.406213 |
0.828614 |
0.127213 |
1.041014 |
0.790613 |
-0.603255 |
1.299546 |
| -1.232895 |
-1.232895 |
0.837897 |
1.192770 |
0.450929 |
-1.741813 |
-0.653622 |
-0.177525 |
1.876793 |
| -0.426871 |
-0.426871 |
1.140176 |
0.182610 |
-0.289479 |
0.824899 |
0.115661 |
-0.531914 |
-0.960658 |
| 0.927928 |
0.927928 |
0.260863 |
0.083166 |
-0.945330 |
-0.779561 |
0.230067 |
-0.819715 |
-1.367425 |
| 0.822418 |
0.822418 |
0.542014 |
-0.193316 |
0.505767 |
-0.425458 |
-0.242475 |
-0.035267 |
-0.022685 |
| 1.674801 |
1.674801 |
-1.232659 |
-2.881999 |
-1.077620 |
-1.378445 |
-0.135817 |
2.060139 |
1.189861 |
| 0.607444 |
0.607444 |
0.523483 |
0.764183 |
-0.733856 |
0.548573 |
0.273106 |
-0.264642 |
0.773705 |
# 字典方式创建DataFrame
df = pd.DataFrame({‘month‘: [1, 4, 7, 10],
‘year‘: [2012, 2014, 2013, 2014],
‘sale‘:[55, 40, 84, 31]})
df
|
month |
year |
sale |
| 0 |
1 |
2012 |
55 |
| 1 |
4 |
2014 |
40 |
| 2 |
7 |
2013 |
84 |
| 3 |
10 |
2014 |
31 |
df.set_index(keys=‘month‘)
|
year |
sale |
| month |
|
|
| 1 |
2012 |
55 |
| 4 |
2014 |
40 |
| 7 |
2013 |
84 |
| 10 |
2014 |
31 |
# 设置2个index, 就是MultiIndex (三维数据结构)
# df.set_index(keys=[‘month‘, ‘year‘])
df.set_index([‘month‘, ‘year‘])
|
|
sale |
| month |
year |
|
| 1 |
2012 |
55 |
| 4 |
2014 |
40 |
| 7 |
2013 |
84 |
| 10 |
2014 |
31 |
1.2 MultiIndex
df_m = df.set_index([‘year‘, ‘month‘])
df_m
|
|
sale |
| year |
month |
|
| 2012 |
1 |
55 |
| 2014 |
4 |
40 |
| 2013 |
7 |
84 |
| 2014 |
10 |
31 |
# index属性
# - names: levers的名称
# - levels: 每个level的列表值
df_m.index
MultiIndex([(2012, 1),
(2014, 4),
(2013, 7),
(2014, 10)],
names=[‘year‘, ‘month‘])
df_m.index.names
FrozenList([‘year‘, ‘month‘])
df_m.index.levels
FrozenList([[2012, 2013, 2014], [1, 4, 7, 10]])
1.3 Series
# 自动生成从0开始的行索引 index
# Data must be 1-dimensional
pd.Series(np.arange(10))
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
dtype: int32
# 手动指定index值
pd.Series([6.7,5.6,3,10,2], index=[‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘])
a 6.7
b 5.6
c 3.0
d 10.0
e 2.0
dtype: float64
# 通过字典创建
se = pd.Series({‘red‘:100, ‘blue‘:200, ‘green‘: 500, ‘yellow‘:1000})
se
red 100
blue 200
green 500
yellow 1000
dtype: int64
# 取索引
se.index
Index([‘red‘, ‘blue‘, ‘green‘, ‘yellow‘], dtype=‘object‘)
# 取array值
se.values
array([ 100, 200, 500, 1000], dtype=int64)
pd.Series(np.random.normal(0, 1, (10)))
0 -0.975747
1 0.021589
2 -0.384579
3 -0.412900
4 0.218133
5 -0.866525
6 -0.777209
7 -1.032130
8 0.202134
9 0.295274
dtype: float64
2.基本数据操作
2.1 索引操作
# 使用pd.read_csv()读取本地数据
data = pd.read_csv(‘./data/stock_day.csv‘)
data
|
open |
high |
close |
low |
volume |
price_change |
p_change |
ma5 |
ma10 |
ma20 |
v_ma5 |
v_ma10 |
v_ma20 |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
22.942 |
22.142 |
22.875 |
53782.64 |
46738.65 |
55576.11 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
22.406 |
21.955 |
22.942 |
40827.52 |
42736.34 |
56007.50 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
21.938 |
21.929 |
23.022 |
35119.58 |
41871.97 |
56372.85 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
21.446 |
21.909 |
23.137 |
35397.58 |
39904.78 |
60149.60 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
21.366 |
21.923 |
23.253 |
33590.21 |
42935.74 |
61716.11 |
0.58 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-03-06 |
13.17 |
14.48 |
14.28 |
13.13 |
179831.72 |
1.12 |
8.51 |
13.112 |
13.112 |
13.112 |
115090.18 |
115090.18 |
115090.18 |
6.16 |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
12.87 |
93180.39 |
0.26 |
2.02 |
12.820 |
12.820 |
12.820 |
98904.79 |
98904.79 |
98904.79 |
3.19 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
12.61 |
67075.44 |
0.20 |
1.57 |
12.707 |
12.707 |
12.707 |
100812.93 |
100812.93 |
100812.93 |
2.30 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
12.52 |
139071.61 |
0.18 |
1.44 |
12.610 |
12.610 |
12.610 |
117681.67 |
117681.67 |
117681.67 |
4.76 |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
12.20 |
96291.73 |
0.32 |
2.62 |
12.520 |
12.520 |
12.520 |
96291.73 |
96291.73 |
96291.73 |
3.30 |
643 rows × 14 columns
# 去除一些列,简化数据
data = data.drop(["ma5","ma10","ma20","v_ma5","v_ma10","v_ma20"], axis=1) # axis=1 去除对应的列,与numpy相反
data
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
0.58 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-03-06 |
13.17 |
14.48 |
14.28 |
13.13 |
179831.72 |
1.12 |
8.51 |
6.16 |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
12.87 |
93180.39 |
0.26 |
2.02 |
3.19 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
12.61 |
67075.44 |
0.20 |
1.57 |
2.30 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
12.52 |
139071.61 |
0.18 |
1.44 |
4.76 |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
12.20 |
96291.73 |
0.32 |
2.62 |
3.30 |
643 rows × 8 columns
2.1.1 直接使用行列索引
# 必须先列后行
data[‘high‘][‘2018-02-27‘]
25.88
# data[‘2018-02-27‘][‘high‘]
2.1.2 使用loc和iloc取索引
# loc取字符串, 先行后列
data.loc[‘2018-02-27‘][‘high‘]
25.88
# 两种取值方式都可以
data.loc[‘2018-02-27‘,‘high‘]
25.88
data.loc[‘2018-02-27‘:‘2018-02-22‘, ‘open‘]
2018-02-27 23.53
2018-02-26 22.80
2018-02-23 22.88
2018-02-22 22.25
Name: open, dtype: float64
# data.loc[‘high‘][‘2018-02-27‘]
# iloc取索引数字,先行后列
data.iloc[:3, 3:5]
|
low |
volume |
| 2018-02-27 |
23.53 |
95578.03 |
| 2018-02-26 |
22.80 |
60985.11 |
| 2018-02-23 |
22.71 |
52914.01 |
2.1.3 使用ix取混合索引
# ix可以去数字和字符串, 先行后列
# 现版本中已被取消
# data.ix[0:4, [‘open‘, ‘close‘, ‘high‘, ‘low‘]]
# 先通过data.index去除索引并切片
data.loc[data.index[0:4], [‘open‘, ‘close‘, ‘high‘, ‘low‘]]
|
open |
close |
high |
low |
| 2018-02-27 |
23.53 |
24.16 |
25.88 |
23.53 |
| 2018-02-26 |
22.80 |
23.53 |
23.78 |
22.80 |
| 2018-02-23 |
22.88 |
22.82 |
23.37 |
22.71 |
| 2018-02-22 |
22.25 |
22.28 |
22.76 |
22.02 |
data.index
Index([‘2018-02-27‘, ‘2018-02-26‘, ‘2018-02-23‘, ‘2018-02-22‘, ‘2018-02-14‘,
‘2018-02-13‘, ‘2018-02-12‘, ‘2018-02-09‘, ‘2018-02-08‘, ‘2018-02-07‘,
...
‘2015-03-13‘, ‘2015-03-12‘, ‘2015-03-11‘, ‘2015-03-10‘, ‘2015-03-09‘,
‘2015-03-06‘, ‘2015-03-05‘, ‘2015-03-04‘, ‘2015-03-03‘, ‘2015-03-02‘],
dtype=‘object‘, length=643)
data.iloc[0:4, data.columns.get_indexer([‘open‘, ‘close‘, ‘high‘, ‘low‘])]
|
open |
close |
high |
low |
| 2018-02-27 |
23.53 |
24.16 |
25.88 |
23.53 |
| 2018-02-26 |
22.80 |
23.53 |
23.78 |
22.80 |
| 2018-02-23 |
22.88 |
22.82 |
23.37 |
22.71 |
| 2018-02-22 |
22.25 |
22.28 |
22.76 |
22.02 |
data.columns.get_indexer([‘close‘])
array([2], dtype=int64)
data
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
0.58 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-03-06 |
13.17 |
14.48 |
14.28 |
13.13 |
179831.72 |
1.12 |
8.51 |
6.16 |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
12.87 |
93180.39 |
0.26 |
2.02 |
3.19 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
12.61 |
67075.44 |
0.20 |
1.57 |
2.30 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
12.52 |
139071.61 |
0.18 |
1.44 |
4.76 |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
12.20 |
96291.73 |
0.32 |
2.62 |
3.30 |
643 rows × 8 columns
2.2 赋值操作
data
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
0.58 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-03-06 |
13.17 |
14.48 |
14.28 |
13.13 |
179831.72 |
1.12 |
8.51 |
6.16 |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
12.87 |
93180.39 |
0.26 |
2.02 |
3.19 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
12.61 |
67075.44 |
0.20 |
1.57 |
2.30 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
12.52 |
139071.61 |
0.18 |
1.44 |
4.76 |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
12.20 |
96291.73 |
0.32 |
2.62 |
3.30 |
643 rows × 8 columns
# 赋值方式1, 直接取对应的属性值
data.volume = 100
data.head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
100 |
0.63 |
2.68 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
100 |
0.54 |
2.42 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
100 |
0.36 |
1.64 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
100 |
0.44 |
2.05 |
0.58 |
# 赋值方式2, 类似于取切片
data[‘low‘] = 100
data.head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
100 |
100 |
0.63 |
2.68 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
100 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
100 |
100 |
0.54 |
2.42 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
100 |
100 |
0.36 |
1.64 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
100 |
100 |
0.44 |
2.05 |
0.58 |
# 直接取出Series
data.open.head()
2018-02-27 23.53
2018-02-26 22.80
2018-02-23 22.88
2018-02-22 22.25
2018-02-14 21.49
Name: open, dtype: float64
# 直接取出Series
data[‘open‘].head()
2018-02-27 23.53
2018-02-26 22.80
2018-02-23 22.88
2018-02-22 22.25
2018-02-14 21.49
Name: open, dtype: float64
2.3 排序
data.head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
100 |
100 |
0.63 |
2.68 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
100 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
100 |
100 |
0.54 |
2.42 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
100 |
100 |
0.36 |
1.64 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
100 |
100 |
0.44 |
2.05 |
0.58 |
2.3.1 以特征值排序
# by --> 传入特征值, 可以传一个或者多个,以列表形式,排前面的作为高优先级,默认升序
data.sort_values(by=‘open‘, ascending=False)
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2015-06-15 |
34.99 |
34.99 |
31.69 |
100 |
100 |
-3.52 |
-10.00 |
6.82 |
| 2015-06-12 |
34.69 |
35.98 |
35.21 |
100 |
100 |
0.82 |
2.38 |
5.47 |
| 2015-06-10 |
34.10 |
36.35 |
33.85 |
100 |
100 |
0.51 |
1.53 |
9.21 |
| 2017-11-01 |
33.85 |
34.34 |
33.83 |
100 |
100 |
-0.61 |
-1.77 |
5.81 |
| 2015-06-11 |
33.17 |
34.98 |
34.39 |
100 |
100 |
0.54 |
1.59 |
5.92 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
100 |
100 |
0.26 |
2.02 |
3.19 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
100 |
100 |
0.20 |
1.57 |
2.30 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
100 |
100 |
0.18 |
1.44 |
4.76 |
| 2015-09-02 |
12.30 |
14.11 |
12.36 |
100 |
100 |
-1.10 |
-8.17 |
2.40 |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
100 |
100 |
0.32 |
2.62 |
3.30 |
643 rows × 8 columns
data.sort_values(by=[‘open‘, ‘high‘], ascending=True)
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
100 |
100 |
0.32 |
2.62 |
3.30 |
| 2015-09-02 |
12.30 |
14.11 |
12.36 |
100 |
100 |
-1.10 |
-8.17 |
2.40 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
100 |
100 |
0.18 |
1.44 |
4.76 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
100 |
100 |
0.20 |
1.57 |
2.30 |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
100 |
100 |
0.26 |
2.02 |
3.19 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-06-11 |
33.17 |
34.98 |
34.39 |
100 |
100 |
0.54 |
1.59 |
5.92 |
| 2017-11-01 |
33.85 |
34.34 |
33.83 |
100 |
100 |
-0.61 |
-1.77 |
5.81 |
| 2015-06-10 |
34.10 |
36.35 |
33.85 |
100 |
100 |
0.51 |
1.53 |
9.21 |
| 2015-06-12 |
34.69 |
35.98 |
35.21 |
100 |
100 |
0.82 |
2.38 |
5.47 |
| 2015-06-15 |
34.99 |
34.99 |
31.69 |
100 |
100 |
-3.52 |
-10.00 |
6.82 |
643 rows × 8 columns
# Series 排序因为只有一个特征值,所以不需要传参
data.close.sort_values()
2015-09-02 12.36
2015-03-02 12.52
2015-03-03 12.70
2015-09-07 12.77
2015-03-04 12.90
...
2017-11-01 33.83
2015-06-10 33.85
2015-06-11 34.39
2017-10-31 34.44
2015-06-12 35.21
Name: close, Length: 643, dtype: float64
2.3.2 以索引排序
# DataFrame 使用sort_index 以索引排序
data.sort_index()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
100 |
100 |
0.32 |
2.62 |
3.30 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
100 |
100 |
0.18 |
1.44 |
4.76 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
100 |
100 |
0.20 |
1.57 |
2.30 |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
100 |
100 |
0.26 |
2.02 |
3.19 |
| 2015-03-06 |
13.17 |
14.48 |
14.28 |
100 |
100 |
1.12 |
8.51 |
6.16 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
100 |
100 |
0.44 |
2.05 |
0.58 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
100 |
100 |
0.36 |
1.64 |
0.90 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
100 |
100 |
0.54 |
2.42 |
1.32 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
100 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
100 |
100 |
0.63 |
2.68 |
2.39 |
643 rows × 8 columns
# Series 排序
data.high.sort_index()
2015-03-02 12.67
2015-03-03 13.06
2015-03-04 12.92
2015-03-05 13.45
2015-03-06 14.48
...
2018-02-14 21.99
2018-02-22 22.76
2018-02-23 23.37
2018-02-26 23.78
2018-02-27 25.88
Name: high, Length: 643, dtype: float64
3. DataFrame运算
3.1 算数运算
data.head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
100 |
100 |
0.63 |
2.68 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
100 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
100 |
100 |
0.54 |
2.42 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
100 |
100 |
0.36 |
1.64 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
100 |
100 |
0.44 |
2.05 |
0.58 |
# 推荐使用pd.方法
data[‘close‘].add(100).head()
2018-02-27 124.16
2018-02-26 123.53
2018-02-23 122.82
2018-02-22 122.28
2018-02-14 121.92
Name: close, dtype: float64
# 使用符号运算
(data.close + 100).head()
2018-02-27 124.16
2018-02-26 123.53
2018-02-23 122.82
2018-02-22 122.28
2018-02-14 121.92
Name: close, dtype: float64
data.close.sub(10).head()
2018-02-27 14.16
2018-02-26 13.53
2018-02-23 12.82
2018-02-22 12.28
2018-02-14 11.92
Name: close, dtype: float64
3.2 逻辑运算
3.2.1 逻辑运算符 （ <, > , |, &)
data.head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
100 |
100 |
0.63 |
2.68 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
100 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
100 |
100 |
0.54 |
2.42 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
100 |
100 |
0.36 |
1.64 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
100 |
100 |
0.44 |
2.05 |
0.58 |
# data.open 返回数据 True, False
# data[data.open] 逻辑判断的结果作为筛选依据
data[‘open‘] > 23
2018-02-27 True
2018-02-26 False
2018-02-23 False
2018-02-22 False
2018-02-14 False
...
2015-03-06 False
2015-03-05 False
2015-03-04 False
2015-03-03 False
2015-03-02 False
Name: open, Length: 643, dtype: bool
data[data.open>23].head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
100 |
100 |
0.63 |
2.68 |
2.39 |
| 2018-02-01 |
23.71 |
23.86 |
22.42 |
100 |
100 |
-1.30 |
-5.48 |
1.66 |
| 2018-01-31 |
23.85 |
23.98 |
23.72 |
100 |
100 |
-0.11 |
-0.46 |
1.23 |
| 2018-01-30 |
23.71 |
24.08 |
23.83 |
100 |
100 |
0.05 |
0.21 |
0.81 |
| 2018-01-29 |
24.40 |
24.63 |
23.77 |
100 |
100 |
-0.73 |
-2.98 |
1.64 |
# 利用与或 (& |)完成逻辑判断
# 优先级问题，多加括号
data[(data.close>23) & (data.close<24)].head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
100 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-05 |
22.45 |
23.39 |
23.27 |
100 |
100 |
0.65 |
2.87 |
1.31 |
| 2018-01-31 |
23.85 |
23.98 |
23.72 |
100 |
100 |
-0.11 |
-0.46 |
1.23 |
| 2018-01-30 |
23.71 |
24.08 |
23.83 |
100 |
100 |
0.05 |
0.21 |
0.81 |
| 2018-01-29 |
24.40 |
24.63 |
23.77 |
100 |
100 |
-0.73 |
-2.98 |
1.64 |
3.2.2 逻辑运算函数
# query（str) 传入字符串
data.query(‘close>23 & close<24‘).head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
100 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-05 |
22.45 |
23.39 |
23.27 |
100 |
100 |
0.65 |
2.87 |
1.31 |
| 2018-01-31 |
23.85 |
23.98 |
23.72 |
100 |
100 |
-0.11 |
-0.46 |
1.23 |
| 2018-01-30 |
23.71 |
24.08 |
23.83 |
100 |
100 |
0.05 |
0.21 |
0.81 |
| 2018-01-29 |
24.40 |
24.63 |
23.77 |
100 |
100 |
-0.73 |
-2.98 |
1.64 |
# isin() 可以传一个值, 也可以传一个列表范围, 判断是否在某个范围内
data[‘open‘].isin([22.80, 23.00])
2018-02-27 False
2018-02-26 True
2018-02-23 False
2018-02-22 False
2018-02-14 False
...
2015-03-06 False
2015-03-05 False
2015-03-04 False
2015-03-03 False
2015-03-02 False
Name: open, Length: 643, dtype: bool
data[data[‘open‘].isin([22.80, 23.00])]
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| 2018-02-26 |
22.8 |
23.78 |
23.53 |
100 |
100 |
0.69 |
3.02 |
1.53 |
| 2018-02-06 |
22.8 |
23.55 |
22.29 |
100 |
100 |
-0.97 |
-4.17 |
1.39 |
| 2017-12-18 |
23.0 |
23.49 |
23.13 |
100 |
100 |
0.12 |
0.52 |
0.74 |
| 2017-07-24 |
22.8 |
23.79 |
23.03 |
100 |
100 |
-0.17 |
-0.73 |
2.59 |
| 2017-06-21 |
23.0 |
23.84 |
23.57 |
100 |
100 |
-0.51 |
-2.12 |
5.13 |
| 2016-01-04 |
22.8 |
22.84 |
20.69 |
100 |
100 |
-2.28 |
-9.93 |
1.60 |
3.3 统计运算
3.3.1 describe()
# describe()方法可以快速的查看DataFrame的整体属性
# 25% - 第一四分位数(Q1),样本中从小到大排列后第25%的数据
# 50% - 中位数
data.describe()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
turnover |
| count |
643.000000 |
643.000000 |
643.000000 |
643.0 |
643.0 |
643.000000 |
643.000000 |
643.000000 |
| mean |
21.272706 |
21.900513 |
21.336267 |
100.0 |
100.0 |
0.018802 |
0.190280 |
2.936190 |
| std |
3.930973 |
4.077578 |
3.942806 |
0.0 |
0.0 |
0.898476 |
4.079698 |
2.079375 |
| min |
12.250000 |
12.670000 |
12.360000 |
100.0 |
100.0 |
-3.520000 |
-10.030000 |
0.040000 |
| 25% |
19.000000 |
19.500000 |
19.045000 |
100.0 |
100.0 |
-0.390000 |
-1.850000 |
1.360000 |
| 50% |
21.440000 |
21.970000 |
21.450000 |
100.0 |
100.0 |
0.050000 |
0.260000 |
2.500000 |
| 75% |
23.400000 |
24.065000 |
23.415000 |
100.0 |
100.0 |
0.455000 |
2.305000 |
3.915000 |
| max |
34.990000 |
36.350000 |
35.210000 |
100.0 |
100.0 |
3.030000 |
10.030000 |
12.560000 |
3.3.2 统计函数
# max(), min()
data.max()
open 34.99
high 36.35
close 35.21
low 100.00
volume 100.00
price_change 3.03
p_change 10.03
turnover 12.56
dtype: float64
data.std()
# data.var()
open 3.930973
high 4.077578
close 3.942806
low 0.000000
volume 0.000000
price_change 0.898476
p_change 4.079698
turnover 2.079375
dtype: float64
data.median()
open 21.44
high 21.97
close 21.45
low 100.00
volume 100.00
price_change 0.05
p_change 0.26
turnover 2.50
dtype: float64
# idxmax ( index-max) 最大值的索引值
data.idxmax()
# data,idxmin()
open 2015-06-15
high 2015-06-10
close 2015-06-12
low 2018-02-27
volume 2018-02-27
price_change 2015-06-09
p_change 2015-08-28
turnover 2017-10-26
dtype: object
3.4 累计统计函数
# 常见累计统计函数为:
# cumsum - 累加
# cummax - 累计取最大值, 新的最大值替换原来的最大值
# cummin - 累计取最小值
# cumprod - 累积
data = data.sort_index()
data.p_change
2015-03-02 2.62
2015-03-03 1.44
2015-03-04 1.57
2015-03-05 2.02
2015-03-06 8.51
...
2018-02-14 2.05
2018-02-22 1.64
2018-02-23 2.42
2018-02-26 3.02
2018-02-27 2.68
Name: p_change, Length: 643, dtype: float64
data.p_change.cumsum()
2015-03-02 2.62
2015-03-03 4.06
2015-03-04 5.63
2015-03-05 7.65
2015-03-06 16.16
...
2018-02-14 112.59
2018-02-22 114.23
2018-02-23 116.65
2018-02-26 119.67
2018-02-27 122.35
Name: p_change, Length: 643, dtype: float64
# 利用Pandas自带的绘图功能, 需要运行2次才能出结果
data.p_change.cumsum().plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1d54ecb2848>
data.p_change.cummax().plot()
<matplotlib.axes._subplots.AxesSubplot at 0x1d54ff73a88>
3.5 自定义函数
# apply(func), fun - lambda函数
data[[‘open‘]] # [[]]取出DataFrame
|
open |
| 2015-03-02 |
12.25 |
| 2015-03-03 |
12.52 |
| 2015-03-04 |
12.80 |
| 2015-03-05 |
12.88 |
| 2015-03-06 |
13.17 |
| ... |
... |
| 2018-02-14 |
21.49 |
| 2018-02-22 |
22.25 |
| 2018-02-23 |
22.88 |
| 2018-02-26 |
22.80 |
| 2018-02-27 |
23.53 |
643 rows × 1 columns
data[[‘open‘]].apply(lambda x: x.max()-x.min()) # 默认axis=0
open 22.74
dtype: float64
4. Pandas内置画图
# DataFrame(x, y, kind=‘line‘)
# kind: 绘图的类型, line, bar, barh, hist, pie, scatter
# DataFrame
data[‘open‘].plot(kind=‘hist‘)
<matplotlib.axes._subplots.AxesSubplot at 0x1d54ffe6c88>
5. 文件读取与存储
5.1 csv文件
# usecols -abs 读取特定列,列表形式传入
# sep=‘,‘ 分隔
# 读取文件
data = pd.read_csv(‘./data/stock_day.csv‘, usecols=[‘open‘, ‘high‘, ‘low‘], sep=‘,‘)
data
|
open |
high |
low |
| 2018-02-27 |
23.53 |
25.88 |
23.53 |
| 2018-02-26 |
22.80 |
23.78 |
22.80 |
| 2018-02-23 |
22.88 |
23.37 |
22.71 |
| 2018-02-22 |
22.25 |
22.76 |
22.02 |
| 2018-02-14 |
21.49 |
21.99 |
21.48 |
| ... |
... |
... |
... |
| 2015-03-06 |
13.17 |
14.48 |
13.13 |
| 2015-03-05 |
12.88 |
13.45 |
12.87 |
| 2015-03-04 |
12.80 |
12.92 |
12.61 |
| 2015-03-03 |
12.52 |
13.06 |
12.52 |
| 2015-03-02 |
12.25 |
12.67 |
12.20 |
643 rows × 3 columns
# 存储文件
# columns ：存储指定列,
# index：是否存储index
data[:10].to_csv(‘./data/test_write_in.csv‘,columns=[‘high‘, ‘low‘], index=False)
5.2 hdf文件
# hdf文件格式是官方推荐的格式，存储读取速度快
# 压缩方式读取速度快，节省空间
# 支持跨平台
day_eps = pd.read_hdf(‘./data/stock_data/day/day_close.h5‘)
# 需要安装tables模块才能显示
# hdf文件不能直接打开，需要导入后才能打开
day_eps
|
000001.SZ |
000002.SZ |
000004.SZ |
000005.SZ |
000006.SZ |
000007.SZ |
000008.SZ |
000009.SZ |
000010.SZ |
000011.SZ |
... |
001965.SZ |
603283.SH |
002920.SZ |
002921.SZ |
300684.SZ |
002922.SZ |
300735.SZ |
603329.SH |
603655.SH |
603080.SH |
| 0 |
16.30 |
17.71 |
4.58 |
2.88 |
14.60 |
2.62 |
4.96 |
4.66 |
5.37 |
6.02 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 1 |
17.02 |
19.20 |
4.65 |
3.02 |
15.97 |
2.65 |
4.95 |
4.70 |
5.37 |
6.27 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 2 |
17.02 |
17.28 |
4.56 |
3.06 |
14.37 |
2.63 |
4.82 |
4.47 |
5.37 |
5.96 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 3 |
16.18 |
16.97 |
4.49 |
2.95 |
13.10 |
2.73 |
4.89 |
4.33 |
5.37 |
5.77 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 4 |
16.95 |
17.19 |
4.55 |
2.99 |
13.18 |
2.77 |
4.97 |
4.42 |
5.37 |
5.92 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2673 |
12.96 |
35.99 |
22.84 |
4.37 |
9.85 |
16.66 |
8.47 |
7.52 |
6.20 |
17.88 |
... |
12.99 |
23.42 |
47.99 |
32.40 |
22.45 |
28.79 |
23.18 |
24.45 |
14.98 |
26.06 |
| 2674 |
13.08 |
35.84 |
23.02 |
4.41 |
9.85 |
16.66 |
8.49 |
7.48 |
6.01 |
17.75 |
... |
12.83 |
25.76 |
45.14 |
35.64 |
24.70 |
31.67 |
25.50 |
26.90 |
16.48 |
28.67 |
| 2675 |
13.47 |
35.67 |
22.40 |
4.32 |
9.85 |
16.66 |
8.49 |
7.38 |
5.97 |
17.45 |
... |
12.20 |
28.34 |
43.21 |
39.20 |
27.17 |
34.84 |
28.05 |
29.59 |
18.13 |
31.54 |
| 2676 |
13.40 |
35.15 |
22.29 |
4.29 |
9.85 |
16.66 |
8.56 |
7.04 |
5.84 |
17.49 |
... |
12.11 |
31.17 |
43.76 |
40.88 |
29.89 |
34.84 |
29.64 |
32.55 |
19.94 |
34.69 |
| 2677 |
13.55 |
35.55 |
22.20 |
4.37 |
9.85 |
16.66 |
8.67 |
7.06 |
5.99 |
17.76 |
... |
11.91 |
34.29 |
41.71 |
39.10 |
32.88 |
34.84 |
27.92 |
31.82 |
21.93 |
38.16 |
2678 rows × 3562 columns
# 存储格式为 .h5
day_eps_test = day_eps.to_hdf(‘./data/day_eps_test.h5‘, key=‘day_eps‘)
pd.read_hdf(‘./data/day_eps_test.h5‘)
|
000001.SZ |
000002.SZ |
000004.SZ |
000005.SZ |
000006.SZ |
000007.SZ |
000008.SZ |
000009.SZ |
000010.SZ |
000011.SZ |
... |
001965.SZ |
603283.SH |
002920.SZ |
002921.SZ |
300684.SZ |
002922.SZ |
300735.SZ |
603329.SH |
603655.SH |
603080.SH |
| 0 |
16.30 |
17.71 |
4.58 |
2.88 |
14.60 |
2.62 |
4.96 |
4.66 |
5.37 |
6.02 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 1 |
17.02 |
19.20 |
4.65 |
3.02 |
15.97 |
2.65 |
4.95 |
4.70 |
5.37 |
6.27 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 2 |
17.02 |
17.28 |
4.56 |
3.06 |
14.37 |
2.63 |
4.82 |
4.47 |
5.37 |
5.96 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 3 |
16.18 |
16.97 |
4.49 |
2.95 |
13.10 |
2.73 |
4.89 |
4.33 |
5.37 |
5.77 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 4 |
16.95 |
17.19 |
4.55 |
2.99 |
13.18 |
2.77 |
4.97 |
4.42 |
5.37 |
5.92 |
... |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2673 |
12.96 |
35.99 |
22.84 |
4.37 |
9.85 |
16.66 |
8.47 |
7.52 |
6.20 |
17.88 |
... |
12.99 |
23.42 |
47.99 |
32.40 |
22.45 |
28.79 |
23.18 |
24.45 |
14.98 |
26.06 |
| 2674 |
13.08 |
35.84 |
23.02 |
4.41 |
9.85 |
16.66 |
8.49 |
7.48 |
6.01 |
17.75 |
... |
12.83 |
25.76 |
45.14 |
35.64 |
24.70 |
31.67 |
25.50 |
26.90 |
16.48 |
28.67 |
| 2675 |
13.47 |
35.67 |
22.40 |
4.32 |
9.85 |
16.66 |
8.49 |
7.38 |
5.97 |
17.45 |
... |
12.20 |
28.34 |
43.21 |
39.20 |
27.17 |
34.84 |
28.05 |
29.59 |
18.13 |
31.54 |
| 2676 |
13.40 |
35.15 |
22.29 |
4.29 |
9.85 |
16.66 |
8.56 |
7.04 |
5.84 |
17.49 |
... |
12.11 |
31.17 |
43.76 |
40.88 |
29.89 |
34.84 |
29.64 |
32.55 |
19.94 |
34.69 |
| 2677 |
13.55 |
35.55 |
22.20 |
4.37 |
9.85 |
16.66 |
8.67 |
7.06 |
5.99 |
17.76 |
... |
11.91 |
34.29 |
41.71 |
39.10 |
32.88 |
34.84 |
27.92 |
31.82 |
21.93 |
38.16 |
2678 rows × 3562 columns
5.3 json文件
# oritent: 读取方式
# lines: 是否按行读取
json_read = pd.read_json("./data/Sarcasm_Headlines_Dataset.json", orient="records", lines=True)
json_read
|
article_link |
headline |
is_sarcastic |
| 0 |
https://www.huffingtonpost.com/entry/versace-b... |
former versace store clerk sues over secret ‘b... |
0 |
| 1 |
https://www.huffingtonpost.com/entry/roseanne-... |
the ‘roseanne‘ revival catches up to our thorn... |
0 |
| 2 |
https://local.theonion.com/mom-starting-to-fea... |
mom starting to fear son‘s web series closest ... |
1 |
| 3 |
https://politics.theonion.com/boehner-just-wan... |
boehner just wants wife to listen, not come up... |
1 |
| 4 |
https://www.huffingtonpost.com/entry/jk-rowlin... |
j.k. rowling wishes snape happy birthday in th... |
0 |
| ... |
... |
... |
... |
| 26704 |
https://www.huffingtonpost.com/entry/american-... |
american politics in moral free-fall |
0 |
| 26705 |
https://www.huffingtonpost.com/entry/americas-... |
america‘s best 20 hikes |
0 |
| 26706 |
https://www.huffingtonpost.com/entry/reparatio... |
reparations and obama |
0 |
| 26707 |
https://www.huffingtonpost.com/entry/israeli-b... |
israeli ban targeting boycott supporters raise... |
0 |
| 26708 |
https://www.huffingtonpost.com/entry/gourmet-g... |
gourmet gifts for the foodie 2014 |
0 |
26709 rows × 3 columns
# lines 表示存储数据分行， 否则全部为一整行
json_read.to_json(‘./data/test.json‘, orient=‘records‘, lines=True)
6.高级处理
6.1 处理缺失值
# 缺失值一般使用nan(not a number)来表示
type(np.nan)
float
# 导入数据
movie = pd.read_csv(‘./data/IMDB-Movie-Data.csv‘)
movie.head()
|
Rank |
Title |
Genre |
Description |
Director |
Actors |
Year |
Runtime (Minutes) |
Rating |
Votes |
Revenue (Millions) |
Metascore |
| 0 |
1 |
Guardians of the Galaxy |
Action,Adventure,Sci-Fi |
A group of intergalactic criminals are forced ... |
James Gunn |
Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... |
2014 |
121 |
8.1 |
757074 |
333.13 |
76.0 |
| 1 |
2 |
Prometheus |
Adventure,Mystery,Sci-Fi |
Following clues to the origin of mankind, a te... |
Ridley Scott |
Noomi Rapace, Logan Marshall-Green, Michael Fa... |
2012 |
124 |
7.0 |
485820 |
126.46 |
65.0 |
| 2 |
3 |
Split |
Horror,Thriller |
Three girls are kidnapped by a man with a diag... |
M. Night Shyamalan |
James McAvoy, Anya Taylor-Joy, Haley Lu Richar... |
2016 |
117 |
7.3 |
157606 |
138.12 |
62.0 |
| 3 |
4 |
Sing |
Animation,Comedy,Family |
In a city of humanoid animals, a hustling thea... |
Christophe Lourdelet |
Matthew McConaughey,Reese Witherspoon, Seth Ma... |
2016 |
108 |
7.2 |
60545 |
270.32 |
59.0 |
| 4 |
5 |
Suicide Squad |
Action,Adventure,Fantasy |
A secret government agency recruits some of th... |
David Ayer |
Will Smith, Jared Leto, Margot Robbie, Viola D... |
2016 |
123 |
6.2 |
393727 |
325.02 |
40.0 |
# 判断缺失值是否存在
# isnull() :nan - True
# notnull()：nan - False
pd.isnull(movie)
|
Rank |
Title |
Genre |
Description |
Director |
Actors |
Year |
Runtime (Minutes) |
Rating |
Votes |
Revenue (Millions) |
Metascore |
| 0 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
| 1 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
| 2 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
| 3 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
| 4 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 995 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
True |
False |
| 996 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
| 997 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
| 998 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
True |
False |
| 999 |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
False |
1000 rows × 12 columns
np.any(pd.isnull(movie)) # 其中有任何一个为True(nan值存在), 则返回True
True
np.all(pd.notnull(movie)) # 所有的元素都非nan
False
6.1.1 丢弃缺失值
# 直接丢弃含有nan的一行数据
data = movie.dropna()
np.any(pd.isnull(data))
False
6.1.2 替换缺失值 （常见：平均值或者0）
# 使用平均值替换
# inplace=True , 表示直接对原来movie值进行修改
data = movie[‘Revenue (Millions)‘].fillna(movie[‘Revenue (Millions)‘].mean())
# inplace默认为False, 返回了新的替换后的一个data数据, 原来的movie中仍含有nan
np.any(pd.isnull(movie[‘Revenue (Millions)‘]))
True
movie[‘Revenue (Millions)‘].fillna(movie[‘Revenue (Millions)‘].mean(), inplace=True)
# movie[‘Revenue (Millions)‘] 中的nan 已经被替换
np.any(pd.isnull(movie[‘Revenue (Millions)‘]))
False
6.1.3 缺失值不是nan
# 全局取消证书验证
# 读取数据
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
wis = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data")
# 先将? 数据替换成nan
# 再对nan进行处理
wis
|
1000025 |
5 |
1 |
1.1 |
1.2 |
2 |
1.3 |
3 |
1.4 |
1.5 |
2.1 |
| 0 |
1002945 |
5 |
4 |
4 |
5 |
7 |
10 |
3 |
2 |
1 |
2 |
| 1 |
1015425 |
3 |
1 |
1 |
1 |
2 |
2 |
3 |
1 |
1 |
2 |
| 2 |
1016277 |
6 |
8 |
8 |
1 |
3 |
4 |
3 |
7 |
1 |
2 |
| 3 |
1017023 |
4 |
1 |
1 |
3 |
2 |
1 |
3 |
1 |
1 |
2 |
| 4 |
1017122 |
8 |
10 |
10 |
8 |
7 |
10 |
9 |
7 |
1 |
4 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 693 |
776715 |
3 |
1 |
1 |
1 |
3 |
2 |
1 |
1 |
1 |
2 |
| 694 |
841769 |
2 |
1 |
1 |
1 |
2 |
1 |
1 |
1 |
1 |
2 |
| 695 |
888820 |
5 |
10 |
10 |
3 |
7 |
3 |
8 |
10 |
2 |
4 |
| 696 |
897471 |
4 |
8 |
6 |
4 |
3 |
4 |
10 |
6 |
1 |
4 |
| 697 |
897471 |
4 |
8 |
8 |
5 |
4 |
5 |
10 |
4 |
1 |
4 |
698 rows × 11 columns
# to_replace: 被替换的值, value:去替换的值
wis = wis.replace(to_replace=‘?‘, value=np.nan)
wis = wis.dropna()
wis
|
1000025 |
5 |
1 |
1.1 |
1.2 |
2 |
1.3 |
3 |
1.4 |
1.5 |
2.1 |
| 0 |
1002945 |
5 |
4 |
4 |
5 |
7 |
10 |
3 |
2 |
1 |
2 |
| 1 |
1015425 |
3 |
1 |
1 |
1 |
2 |
2 |
3 |
1 |
1 |
2 |
| 2 |
1016277 |
6 |
8 |
8 |
1 |
3 |
4 |
3 |
7 |
1 |
2 |
| 3 |
1017023 |
4 |
1 |
1 |
3 |
2 |
1 |
3 |
1 |
1 |
2 |
| 4 |
1017122 |
8 |
10 |
10 |
8 |
7 |
10 |
9 |
7 |
1 |
4 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 693 |
776715 |
3 |
1 |
1 |
1 |
3 |
2 |
1 |
1 |
1 |
2 |
| 694 |
841769 |
2 |
1 |
1 |
1 |
2 |
1 |
1 |
1 |
1 |
2 |
| 695 |
888820 |
5 |
10 |
10 |
3 |
7 |
3 |
8 |
10 |
2 |
4 |
| 696 |
897471 |
4 |
8 |
6 |
4 |
3 |
4 |
10 |
6 |
1 |
4 |
| 697 |
897471 |
4 |
8 |
8 |
5 |
4 |
5 |
10 |
4 |
1 |
4 |
682 rows × 11 columns
6.2 数据离散化
# 数据离散化可以简化数据结构,将数据划分到若干离散的区间,可以简化数据结构, 常用于搭配one-hot编码
# 获取数据
data = pd.read_csv("./data/stock_day.csv")
data_p= data[‘p_change‘]
data_p
2018-02-27 2.68
2018-02-26 3.02
2018-02-23 2.42
2018-02-22 1.64
2018-02-14 2.05
...
2015-03-06 8.51
2015-03-05 2.02
2015-03-04 1.57
2015-03-03 1.44
2015-03-02 2.62
Name: p_change, Length: 643, dtype: float64
# pd.qcut() 智能分组
# q: 分组数量
q_cut = pd.qcut(data_p, q=10)
q_cut
2018-02-27 (1.738, 2.938]
2018-02-26 (2.938, 5.27]
2018-02-23 (1.738, 2.938]
2018-02-22 (0.94, 1.738]
2018-02-14 (1.738, 2.938]
...
2015-03-06 (5.27, 10.03]
2015-03-05 (1.738, 2.938]
2015-03-04 (0.94, 1.738]
2015-03-03 (0.94, 1.738]
2015-03-02 (1.738, 2.938]
Name: p_change, Length: 643, dtype: category
Categories (10, interval[float64]): [(-10.030999999999999, -4.836] < (-4.836, -2.444] < (-2.444, -1.352] < (-1.352, -0.462] ... (0.94, 1.738] < (1.738, 2.938] < (2.938, 5.27] < (5.27, 10.03]]
# value_counts(): 每个分组区间内的数据数量
q_cut.value_counts()
(5.27, 10.03] 65
(0.26, 0.94] 65
(-0.462, 0.26] 65
(-10.030999999999999, -4.836] 65
(2.938, 5.27] 64
(1.738, 2.938] 64
(-1.352, -0.462] 64
(-2.444, -1.352] 64
(-4.836, -2.444] 64
(0.94, 1.738] 63
Name: p_change, dtype: int64
# pd.cut(data, bins): 自己指定分组区间
bins = [-100, -7, -5, -3, 0, 3, 5, 7, 100]
cut = pd.cut(data_p, bins=bins)
cut
2018-02-27 (0, 3]
2018-02-26 (3, 5]
2018-02-23 (0, 3]
2018-02-22 (0, 3]
2018-02-14 (0, 3]
...
2015-03-06 (7, 100]
2015-03-05 (0, 3]
2015-03-04 (0, 3]
2015-03-03 (0, 3]
2015-03-02 (0, 3]
Name: p_change, Length: 643, dtype: category
Categories (8, interval[int64]): [(-100, -7] < (-7, -5] < (-5, -3] < (-3, 0] < (0, 3] < (3, 5] < (5, 7] < (7, 100]]
cut.value_counts()
(0, 3] 215
(-3, 0] 188
(3, 5] 57
(-5, -3] 51
(7, 100] 35
(5, 7] 35
(-100, -7] 34
(-7, -5] 28
Name: p_change, dtype: int64
# get_dummies() 取独热矩阵
pd.get_dummies(q_cut)
|
(-10.030999999999999, -4.836] |
(-4.836, -2.444] |
(-2.444, -1.352] |
(-1.352, -0.462] |
(-0.462, 0.26] |
(0.26, 0.94] |
(0.94, 1.738] |
(1.738, 2.938] |
(2.938, 5.27] |
(5.27, 10.03] |
| 2018-02-27 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| 2018-02-26 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
| 2018-02-23 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| 2018-02-22 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
| 2018-02-14 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-03-06 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| 2015-03-05 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| 2015-03-04 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
| 2015-03-03 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
| 2015-03-02 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
643 rows × 10 columns
data_dummy = pd.get_dummies(q_cut)
6.3 数据拼接
6.3.1 pd.concat()
# 不指定axis 可能会造成拼接错位,产生很多nan
pd.concat([data, data_dummy], axis=1)
|
open |
high |
close |
low |
volume |
price_change |
p_change |
ma5 |
ma10 |
ma20 |
... |
(-10.030999999999999, -4.836] |
(-4.836, -2.444] |
(-2.444, -1.352] |
(-1.352, -0.462] |
(-0.462, 0.26] |
(0.26, 0.94] |
(0.94, 1.738] |
(1.738, 2.938] |
(2.938, 5.27] |
(5.27, 10.03] |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
22.942 |
22.142 |
22.875 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
22.406 |
21.955 |
22.942 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
21.938 |
21.929 |
23.022 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
21.446 |
21.909 |
23.137 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
21.366 |
21.923 |
23.253 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-03-06 |
13.17 |
14.48 |
14.28 |
13.13 |
179831.72 |
1.12 |
8.51 |
13.112 |
13.112 |
13.112 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
12.87 |
93180.39 |
0.26 |
2.02 |
12.820 |
12.820 |
12.820 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
12.61 |
67075.44 |
0.20 |
1.57 |
12.707 |
12.707 |
12.707 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
12.52 |
139071.61 |
0.18 |
1.44 |
12.610 |
12.610 |
12.610 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
12.20 |
96291.73 |
0.32 |
2.62 |
12.520 |
12.520 |
12.520 |
... |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
643 rows × 24 columns
6.3.2 pd.merge()
# 获取数据
left = pd.DataFrame({‘key1‘: [‘K0‘, ‘K0‘, ‘K1‘, ‘K2‘],
‘key2‘: [‘K0‘, ‘K1‘, ‘K0‘, ‘K1‘],
‘A‘: [‘A0‘, ‘A1‘, ‘A2‘, ‘A3‘],
‘B‘: [‘B0‘, ‘B1‘, ‘B2‘, ‘B3‘]})
right = pd.DataFrame({‘key1‘: [‘K0‘, ‘K1‘, ‘K1‘, ‘K2‘],
‘key2‘: [‘K0‘, ‘K0‘, ‘K0‘, ‘K0‘],
‘C‘: [‘C0‘, ‘C1‘, ‘C2‘, ‘C3‘],
‘D‘: [‘D0‘, ‘D1‘, ‘D2‘, ‘D3‘]})
# 默认内连接
# on: 以什么作为键来拼接
result = pd.merge(left, right, on=[‘key1‘, ‘key2‘])
# 内连接就是取交集
result
|
key1 |
key2 |
A |
B |
C |
D |
| 0 |
K0 |
K0 |
A0 |
B0 |
C0 |
D0 |
| 1 |
K1 |
K0 |
A2 |
B2 |
C1 |
D1 |
| 2 |
K1 |
K0 |
A2 |
B2 |
C2 |
D2 |
# 外连接
pd.merge(left, right, on=[‘key1‘, ‘key2‘], how=‘outer‘)
|
key1 |
key2 |
A |
B |
C |
D |
| 0 |
K0 |
K0 |
A0 |
B0 |
C0 |
D0 |
| 1 |
K0 |
K1 |
A1 |
B1 |
NaN |
NaN |
| 2 |
K1 |
K0 |
A2 |
B2 |
C1 |
D1 |
| 3 |
K1 |
K0 |
A2 |
B2 |
C2 |
D2 |
| 4 |
K2 |
K1 |
A3 |
B3 |
NaN |
NaN |
| 5 |
K2 |
K0 |
NaN |
NaN |
C3 |
D3 |
# 左连接
pd.merge(left, right, on=[‘key1‘, ‘key2‘], how=‘left‘)
|
key1 |
key2 |
A |
B |
C |
D |
| 0 |
K0 |
K0 |
A0 |
B0 |
C0 |
D0 |
| 1 |
K0 |
K1 |
A1 |
B1 |
NaN |
NaN |
| 2 |
K1 |
K0 |
A2 |
B2 |
C1 |
D1 |
| 3 |
K1 |
K0 |
A2 |
B2 |
C2 |
D2 |
| 4 |
K2 |
K1 |
A3 |
B3 |
NaN |
NaN |
# 右连接
pd.merge(left, right, on=[‘key1‘, ‘key2‘], how=‘right‘)
|
key1 |
key2 |
A |
B |
C |
D |
| 0 |
K0 |
K0 |
A0 |
B0 |
C0 |
D0 |
| 1 |
K1 |
K0 |
A2 |
B2 |
C1 |
D1 |
| 2 |
K1 |
K0 |
A2 |
B2 |
C2 |
D2 |
| 3 |
K2 |
K0 |
NaN |
NaN |
C3 |
D3 |
6.4 交叉表和透视表
data.head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
ma5 |
ma10 |
ma20 |
v_ma5 |
v_ma10 |
v_ma20 |
turnover |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
22.942 |
22.142 |
22.875 |
53782.64 |
46738.65 |
55576.11 |
2.39 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
22.406 |
21.955 |
22.942 |
40827.52 |
42736.34 |
56007.50 |
1.53 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
21.938 |
21.929 |
23.022 |
35119.58 |
41871.97 |
56372.85 |
1.32 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
21.446 |
21.909 |
23.137 |
35397.58 |
39904.78 |
60149.60 |
0.90 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
21.366 |
21.923 |
23.253 |
33590.21 |
42935.74 |
61716.11 |
0.58 |
# 将date.index 转化为datetime格式
date = pd.to_datetime(data.index).weekday
data[‘week‘] = date
data
|
open |
high |
close |
low |
volume |
price_change |
p_change |
ma5 |
ma10 |
ma20 |
v_ma5 |
v_ma10 |
v_ma20 |
turnover |
posi_neg |
week |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
22.942 |
22.142 |
22.875 |
53782.64 |
46738.65 |
55576.11 |
2.39 |
1 |
1 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
22.406 |
21.955 |
22.942 |
40827.52 |
42736.34 |
56007.50 |
1.53 |
1 |
0 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
21.938 |
21.929 |
23.022 |
35119.58 |
41871.97 |
56372.85 |
1.32 |
1 |
4 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
21.446 |
21.909 |
23.137 |
35397.58 |
39904.78 |
60149.60 |
0.90 |
1 |
3 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
21.366 |
21.923 |
23.253 |
33590.21 |
42935.74 |
61716.11 |
0.58 |
1 |
2 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 2015-03-06 |
13.17 |
14.48 |
14.28 |
13.13 |
179831.72 |
1.12 |
8.51 |
13.112 |
13.112 |
13.112 |
115090.18 |
115090.18 |
115090.18 |
6.16 |
1 |
4 |
| 2015-03-05 |
12.88 |
13.45 |
13.16 |
12.87 |
93180.39 |
0.26 |
2.02 |
12.820 |
12.820 |
12.820 |
98904.79 |
98904.79 |
98904.79 |
3.19 |
1 |
3 |
| 2015-03-04 |
12.80 |
12.92 |
12.90 |
12.61 |
67075.44 |
0.20 |
1.57 |
12.707 |
12.707 |
12.707 |
100812.93 |
100812.93 |
100812.93 |
2.30 |
1 |
2 |
| 2015-03-03 |
12.52 |
13.06 |
12.70 |
12.52 |
139071.61 |
0.18 |
1.44 |
12.610 |
12.610 |
12.610 |
117681.67 |
117681.67 |
117681.67 |
4.76 |
1 |
1 |
| 2015-03-02 |
12.25 |
12.67 |
12.52 |
12.20 |
96291.73 |
0.32 |
2.62 |
12.520 |
12.520 |
12.520 |
96291.73 |
96291.73 |
96291.73 |
3.30 |
1 |
0 |
643 rows × 16 columns
# 把p_change 划分为0, 1两类
data[‘posi_neg‘] = np.where(data[‘p_change‘]>0, 1, 0)
data.head()
|
open |
high |
close |
low |
volume |
price_change |
p_change |
ma5 |
ma10 |
ma20 |
v_ma5 |
v_ma10 |
v_ma20 |
turnover |
posi_neg |
week |
| 2018-02-27 |
23.53 |
25.88 |
24.16 |
23.53 |
95578.03 |
0.63 |
2.68 |
22.942 |
22.142 |
22.875 |
53782.64 |
46738.65 |
55576.11 |
2.39 |
1 |
1 |
| 2018-02-26 |
22.80 |
23.78 |
23.53 |
22.80 |
60985.11 |
0.69 |
3.02 |
22.406 |
21.955 |
22.942 |
40827.52 |
42736.34 |
56007.50 |
1.53 |
1 |
0 |
| 2018-02-23 |
22.88 |
23.37 |
22.82 |
22.71 |
52914.01 |
0.54 |
2.42 |
21.938 |
21.929 |
23.022 |
35119.58 |
41871.97 |
56372.85 |
1.32 |
1 |
4 |
| 2018-02-22 |
22.25 |
22.76 |
22.28 |
22.02 |
36105.01 |
0.36 |
1.64 |
21.446 |
21.909 |
23.137 |
35397.58 |
39904.78 |
60149.60 |
0.90 |
1 |
3 |
| 2018-02-14 |
21.49 |
21.99 |
21.92 |
21.48 |
23331.04 |
0.44 |
2.05 |
21.366 |
21.923 |
23.253 |
33590.21 |
42935.74 |
61716.11 |
0.58 |
1 |
2 |
# 手动构造交叉表
count = pd.crosstab(data[‘week‘], data[‘posi_neg‘])
sum = count.sum(axis=1).astype(np.float32)
pro = count.div(sum, axis=0)
pro.plot(kind=‘bar‘, stacked=True)
<matplotlib.axes._subplots.AxesSubplot at 0x1d55bc28408>
# 自动构造交叉表
data.pivot_table([‘posi_neg‘], index=‘week‘)
|
posi_neg |
| week |
|
| 0 |
0.496000 |
| 1 |
0.580153 |
| 2 |
0.537879 |
| 3 |
0.507812 |
| 4 |
0.535433 |
6.5 分组和聚合
6.5.1 pd.groupby()
# 创建数据
col =pd.DataFrame({‘color‘: [‘white‘,‘red‘,‘green‘,‘red‘,‘green‘], ‘object‘: [‘pen‘,‘pencil‘,‘pencil‘,‘ashtray‘,‘pen‘],‘price1‘:[5.56,4.20,1.30,0.56,2.75],‘price2‘:[4.75,4.12,1.60,0.75,3.15]})
col
|
color |
object |
price1 |
price2 |
| 0 |
white |
pen |
5.56 |
4.75 |
| 1 |
red |
pencil |
4.20 |
4.12 |
| 2 |
green |
pencil |
1.30 |
1.60 |
| 3 |
red |
ashtray |
0.56 |
0.75 |
| 4 |
green |
pen |
2.75 |
3.15 |
# DataFrame 分组, 推荐使用
# 单独的分组没有意义,进行聚合(求值)才有价值
col.groupby([‘color‘])[‘price1‘].mean()
color
green 2.025
red 2.380
white 5.560
Name: price1, dtype: float64
# 如果设置as_index= False 会创建一列新索引
col.groupby([‘color‘], as_index= False)[‘price1‘].mean()
|
color |
price1 |
| 0 |
green |
2.025 |
| 1 |
red |
2.380 |
| 2 |
white |
5.560 |
col.price1
0 5.56
1 4.20
2 1.30
3 0.56
4 2.75
Name: price1, dtype: float64
# Series 分组
col.price1.groupby(col[‘color‘]).mean()
color
green 2.025
red 2.380
white 5.560
Name: price1, dtype: float64
6.5.2 分组实例
# 获取数据
starbucks = pd.read_csv("./data/starbucks/directory.csv")
starbucks.head()
|
Brand |
Store Number |
Store Name |
Ownership Type |
Street Address |
City |
State/Province |
Country |
Postcode |
Phone Number |
Timezone |
Longitude |
Latitude |
| 0 |
Starbucks |
47370-257954 |
Meritxell, 96 |
Licensed |
Av. Meritxell, 96 |
Andorra la Vella |
7 |
AD |
AD500 |
376818720 |
GMT+1:00 Europe/Andorra |
1.53 |
42.51 |
| 1 |
Starbucks |
22331-212325 |
Ajman Drive Thru |
Licensed |
1 Street 69, Al Jarf |
Ajman |
AJ |
AE |
NaN |
NaN |
GMT+04:00 Asia/Dubai |
55.47 |
25.42 |
| 2 |
Starbucks |
47089-256771 |
Dana Mall |
Licensed |
Sheikh Khalifa Bin Zayed St. |
Ajman |
AJ |
AE |
NaN |
NaN |
GMT+04:00 Asia/Dubai |
55.47 |
25.39 |
| 3 |
Starbucks |
22126-218024 |
Twofour 54 |
Licensed |
Al Salam Street |
Abu Dhabi |
AZ |
AE |
NaN |
NaN |
GMT+04:00 Asia/Dubai |
54.38 |
24.48 |
| 4 |
Starbucks |
17127-178586 |
Al Ain Tower |
Licensed |
Khaldiya Area, Abu Dhabi Island |
Abu Dhabi |
AZ |
AE |
NaN |
NaN |
GMT+04:00 Asia/Dubai |
54.54 |
24.51 |
# 以一个值为分组依据
starbucks.groupby([‘Country‘]).count()
|
Brand |
Store Number |
Store Name |
Ownership Type |
Street Address |
City |
State/Province |
Postcode |
Phone Number |
Timezone |
Longitude |
Latitude |
| Country |
|
|
|
|
|
|
|
|
|
|
|
|
| AD |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
| AE |
144 |
144 |
144 |
144 |
144 |
144 |
144 |
24 |
78 |
144 |
144 |
144 |
| AR |
108 |
108 |
108 |
108 |
108 |
108 |
108 |
100 |
29 |
108 |
108 |
108 |
| AT |
18 |
18 |
18 |
18 |
18 |
18 |
18 |
18 |
17 |
18 |
18 |
18 |
| AU |
22 |
22 |
22 |
22 |
22 |
22 |
22 |
22 |
0 |
22 |
22 |
22 |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| TT |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
0 |
3 |
3 |
3 |
| TW |
394 |
394 |
394 |
394 |
394 |
394 |
394 |
365 |
39 |
394 |
394 |
394 |
| US |
13608 |
13608 |
13608 |
13608 |
13608 |
13608 |
13608 |
13607 |
13122 |
13608 |
13608 |
13608 |
| VN |
25 |
25 |
25 |
25 |
25 |
25 |
25 |
25 |
23 |
25 |
25 |
25 |
| ZA |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
2 |
3 |
3 |
3 |
73 rows × 12 columns
starbucks_count = starbucks.groupby([‘Country‘]).count()
starbucks_count[‘Brand‘].plot(kind=‘bar‘, figsize=(20, 8))
<matplotlib.axes._subplots.AxesSubplot at 0x1d563dbd148>
# 为了阅读方便,对数据进行排序后画图
starbucks_count.sort_values(by=‘Brand‘, ascending=False).head(20)[‘Brand‘].plot(kind=‘bar‘, figsize=(20, 8))
<matplotlib.axes._subplots.AxesSubplot at 0x1d55ca68f48>
# 多种分组依据
starbucks.groupby([‘Country‘, ‘State/Province‘]).count().head(20)
|
|
Brand |
Store Number |
Store Name |
Ownership Type |
Street Address |
City |
Postcode |
Phone Number |
Timezone |
Longitude |
Latitude |
| Country |
State/Province |
|
|
|
|
|
|
|
|
|
|
|
| AD |
7 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
| AE |
AJ |
2 |
2 |
2 |
2 |
2 |
2 |
0 |
0 |
2 |
2 |
2 |
| AZ |
48 |
48 |
48 |
48 |
48 |
48 |
7 |
20 |
48 |
48 |
48 |
| DU |
82 |
82 |
82 |
82 |
82 |
82 |
16 |
50 |
82 |
82 |
82 |
| FU |
2 |
2 |
2 |
2 |
2 |
2 |
1 |
0 |
2 |
2 |
2 |
| RK |
3 |
3 |
3 |
3 |
3 |
3 |
0 |
3 |
3 |
3 |
3 |
| SH |
6 |
6 |
6 |
6 |
6 |
6 |
0 |
5 |
6 |
6 |
6 |
| UQ |
1 |
1 |
1 |
1 |
1 |
1 |
0 |
0 |
1 |
1 |
1 |
| AR |
B |
21 |
21 |
21 |
21 |
21 |
21 |
18 |
5 |
21 |
21 |
21 |
| C |
73 |
73 |
73 |
73 |
73 |
73 |
71 |
24 |
73 |
73 |
73 |
| M |
5 |
5 |
5 |
5 |
5 |
5 |
2 |
0 |
5 |
5 |
5 |
| S |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
0 |
3 |
3 |
3 |
| X |
6 |
6 |
6 |
6 |
6 |
6 |
6 |
0 |
6 |
6 |
6 |
| AT |
3 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
| 5 |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
3 |
| 9 |
14 |
14 |
14 |
14 |
14 |
14 |
14 |
13 |
14 |
14 |
14 |
| AU |
NSW |
9 |
9 |
9 |
9 |
9 |
9 |
9 |
0 |
9 |
9 |
9 |
| QLD |
8 |
8 |
8 |
8 |
8 |
8 |
8 |
0 |
8 |
8 |
8 |
| VIC |
5 |
5 |
5 |
5 |
5 |
5 |
5 |
0 |
5 |
5 |
5 |
| AW |
AW |
3 |
3 |
3 |
3 |
3 |
3 |
0 |
3 |
3 |
3 |
3 |
以上是关于Pandas 精简实例入门的主要内容，如果未能解决你的问题，请参考以下文章
pandas入门
短视频学习 - 15pandas入门之数据结构-Series
Dart语言精简入门介绍
[原]实例-简单设计&精简代码&复用代码
[读书笔记] Python数据分析 pandas入门
H5实例教学--从AnimateCC到CreateJS入门