数据帧中的多个计数和中值

Question

我正在尝试同时在一个程序中执行多个操作。我有一个数据框，其中有Dates我不知道开始和结束，我想找到：

1. 数据集的总天数
2. 总小时数
3. 计数的中位数
4. 为每天/日期的中位数编写单独的输出。
5. 如果可能的话，以最可能简单的方式进行中位数。

输入：GB 大小的大文件中的几行

2004-01-05,16:00:00,17:00:00,Mon,10766,656
2004-01-05,17:00:00,18:00:00,Mon,12223,670
2004-01-05,18:00:00,19:00:00,Mon,12646,710
2004-01-05,19:00:00,20:00:00,Mon,19269,778
2004-01-05,20:00:00,21:00:00,Mon,20504,792
2004-01-05,21:00:00,22:00:00,Mon,16553,783
2004-01-05,22:00:00,23:00:00,Mon,18944,790
2004-01-05,23:00:00,00:00:00,Mon,17534,750
2004-01-06,00:00:00,01:00:00,Tue,17262,747
2004-01-06,01:00:00,02:00:00,Tue,19072,777
2004-01-06,02:00:00,03:00:00,Tue,18275,785
2004-01-06,03:00:00,04:00:00,Tue,13589,757
2004-01-06,04:00:00,05:00:00,Tue,16053,735

开始和结束日期未知。

编辑：预期输出：1 将只有一行结果

days,hours,median,median-of-median
2,17262,13,17398

中

位数比是输出 2 中median列的中值

预期输出：2，将具有每个日期的中位数，用于查找中位数

date,median
2004-01-05,17534
2004-01-06,17262

法典：

import pandas as pd 
from datetime import datetime
df = pd.read_csv('one_hour.csv')
df.columns = ['date', 'startTime', 'endTime', 'day', 'count', 'unique']
date_count = df.count(['date'])
all_median = df.median(['count'])
all_hours = df.count(['startTime'])
med_med = df.groupby(['date','count']).median()
print date_count
print all_median
print all_hours
stats = ['date_count', 'all_median', 'all_hours', 'median-of-median']
stats.to_csv('stats_all.csv', index=False)
med_med.to_csv('med_day.csv', index=False, header=False)

显然，代码没有给出应有的结果。

错误如下所示。

错误：

Traceback (most recent call last):
  File "day_median.py", line 8, in <module>
    all_median = df.median(['count'])
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 5310, in stat_func
    numeric_only=numeric_only)
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 4760, in _reduce
    axis = self._get_axis_number(axis)
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 308, in _get_axis_number
    axis = self._AXIS_ALIASES.get(axis, axis)
TypeError: unhashable type: 'list'

Answer 1

IIUC可能有助于改变：

date_count = df.count(['date'])
all_median = df.median(['count'])
all_hours = df.count(['startTime'])

自：

date_count = df['date'].count()
all_median = df['count'].median()
all_hours = df['startTime'].count()
print (date_count)
print (all_median)
print (all_hours)
13
17262.0
13

如果需要，从date、count和startTime列统计。

通过评论编辑：

如果需要计数列的唯一值，请使用nunique：

date_count = df['date'].nunique()
print (date_count)
2

数据帧stats：

cols = ['date_count', 'all_median', 'all_hours']
stats = pd.DataFrame([[date_count, all_median, all_hours]], columns = cols)
print (stats)
   date_count  all_median  all_hours
0           2     17262.0         13