DA

来自Jack's Lab
(版本间的差异)
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(描述性统计)
(描述性统计)
第25行: 第25行:
 
>>> p.mean()
 
>>> p.mean()
 
Press    3685.248525
 
Press    3685.248525
dtype: float64
 
  
 
>>> stats.trimboth(p['Press'],0.1).mean()
 
>>> stats.trimboth(p['Press'],0.1).mean()
第34行: 第33行:
 
>>> p.median()
 
>>> p.median()
 
Press    3677.105
 
Press    3677.105
dtype: float64
 
  
 
>>> p.describe()
 
>>> p.describe()

2020年2月17日 (一) 00:00的版本

目录

1 Overview


2 描述性统计

import pands as pd
from scipy import stats

>>> p = pd.read_csv('../DA/da01-press.csv', index_col='time', date_parser=lambda x: pd.to_datetime(float(x)))
>>> p = p.drop(columns=['name'])
>>> p.mean()
Press    3685.248525

>>> stats.trimboth(p['Press'],0.1).mean()
3680.078265306123
>>> stats.trim_mean(p, 0.1)
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


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(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





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