DA
来自Jack's Lab
目录 |
1 Overview
2 描述性统计
2.1 位置量化
直观的:
import numpy as np import matplotlib.pyplot as plt from scipy import stats d = np.array([1, 2, 2, 100, 3, 3, 6, 8]) np.mean(d); stats.trim_mean(d, 0.2); np.median(d) 15.625 4.0 3.0 >>> plt.plot(d, 'o'); plt.show()
实际的:
import pandas as pd from scipy import stats p = pd.read_csv('../DA/data/da01-press.csv', index_col='time', date_parser=lambda x: pd.to_datetime(float(x)+28800000000000)) p = p.drop(columns=['name']) p.mean() Press 3685.248525 stats.trim_mean(p, 0.1) # stats.trimboth(p['Press'],0.1).mean() array([3680.07826531]) p.median() Press 3677.105 p.describe() Press count 122.000000 mean 3685.248525 std 123.990939 min 3484.480000 25% 3618.402500 50% 3677.105000 75% 3747.742500 max 4672.060000
2.2 分散性量化
>>> d = np.array([3, 1, 5, 3, 15, 6, 7, 2]) >>> meanl = np.array([np.mean(d)]*len(d)); trimmeanl = np.array([stats.trim_mean(d, 0.2)]*len(d)); medianl = np.array([np.median(d)]*len(d)) >>> iqrv = np.array([stats.iqr(d)]*len(d)) >>> down = medianl -iqrv; up = medianl+iqrv >>> plt.plot(d,'o',color='C1'); plt.plot(meanl, ':C2', label='Mean'); plt.plot(trimmeanl, ':r', label='Trim mean'); plt.plot(medianl, '-g', label='Meidan') >>> plt.plot(up, '-C1'); plt.plot(down, '-C1') >>> plt.legend(); plt.grid(); plt.show()
2.3 相关性估计
>>> t1 = pd.read_csv('../DA/data/da02-temp-0948.csv', index_col='time', date_parser=lambda x: pd.to_datetime(float(x)+28800000000000)) >>> t2 = pd.read_csv('../DA/data/da02-temp-0019.csv', index_col='time', date_parser=lambda x: pd.to_datetime(float(x)+28800000000000)) >>> plt.plot(t1.index, t1['Temp'], label='t1') >>> plt.plot(t2.index, t2['Temp'], label='t2') >>> plt.plot(t1['Temp'].index,t3, label='t3') >>> plt.legend(); plt.show()
3 探索数据分布
3.1 histogram
>>> import pandas as pd >>> a = pd.Series([0.1, 1.2, 1.2, 2.1, 2.1, 3, 2,]) >>> a.value_counts() 2.1 2 1.2 2 0.1 1 2.0 1 3.0 1 >>> a.value_counts(normalize=True) 2.1 0.285714 1.2 0.285714 0.1 0.142857 2.0 0.142857 3.0 0.142857
3.2 bar
每天统计事件 A 发生的次数,其实已经做了单个窗口是 24 小时、bins 持续自然增长的频数运算。这类数据直接用柱状图 (bar) 显示一下即可:
import pandas as pd import matplotlib.pyplot as plt import matplotlib.dates as mdate hb = pd.read_csv("../DA/data/ncp-hb-new.csv", index_col='Date', parse_dates=True, skipinitialspace=True) cn = pd.read_csv("../DA/data/ncp-cn-new.csv", index_col='Date', parse_dates=True, skipinitialspace=True) xhb = cn-hb plt.gca().xaxis.set_major_formatter(mdate.DateFormatter('%m-%d')) #plt.bar(hb.index, hb['Confirmed'].values) plt.bar(xhb.index, xhb['Confirmed'].values) plt.show()
同时显示湖北和非湖北柱状图:
plt.bar(xhb.index, xhb_cf, align='edge', width=0.3, label='Outside Hubei') plt.bar(hb.index, hb['Confirmed'].values, align='edge', width=-0.4, label='Hubei') plt.legend() plt.gcf().autofmt_xdate() plt.show()
4 时序数据分析
>>> x = pd.date_range('2020-1-9','2020-2-15',freq='1d') >>> print(x) DatetimeIndex(['2020-01-09', '2020-01-10', '2020-01-11', '2020-01-12', '2020-01-13', '2020-01-14', '2020-01-15', '2020-01-16', '2020-01-17', '2020-01-18', '2020-01-19', '2020-01-20', '2020-01-21', '2020-01-22', '2020-01-23', '2020-01-24', '2020-01-25', '2020-01-26', '2020-01-27', '2020-01-28', '2020-01-29', '2020-01-30', '2020-01-31', '2020-02-01', '2020-02-02', '2020-02-03', '2020-02-04', '2020-02-05', '2020-02-06', '2020-02-07', '2020-02-08', '2020-02-09', '2020-02-10', '2020-02-11', '2020-02-12', '2020-02-13', '2020-02-14', '2020-02-15'], dtype='datetime64[ns]', freq='D')
5 Reference
- Numpy API reference
- Pandas API reference
- matplotlib Gallery
- Change the Colors Changes to the default style
- matplotlib.pyplot.plot()
- matplotlib.pyplot.figure()
- Time Series Analysis Example
- Introduction to Data Science
- Data Visualization tutorial
- FlowingData Tutorials