DA
来自Jack's Lab
(版本间的差异)
(→时序数据分析) |
(→bar) |
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第48行: | 第48行: | ||
plt.bar(hb.index, hb['Confirmed'].values, align='edge', width=-0.4) | plt.bar(hb.index, hb['Confirmed'].values, align='edge', width=-0.4) | ||
plt.bar(xhb.index, xhb_cf, align='edge', width=0.4) | plt.bar(xhb.index, xhb_cf, align='edge', width=0.4) | ||
+ | plt.gcf().autofmt_xdate() | ||
plt.show() | plt.show() | ||
</source> | </source> |
2020年2月16日 (日) 20:14的版本
目录 |
1 Overview
2 描述性统计
3 探索数据分布
3.1 bar
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(hb.index, hb['Confirmed'].values, align='edge', width=-0.4) plt.bar(xhb.index, xhb_cf, align='edge', width=0.4) 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