Python pandas秘籍第四、五、六章个人笔记

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Python 3.7.6 (default, Jan  8 2020, 20:23:39) [MSC v.1916 64 bit (AMD64)]
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IPython 7.12.0 -- An enhanced Interactive Python.

import pandas as pd

第四章

#4.1 DataFrame 中刚添加 weekday 列

bikes = pd.read_csv('bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')
bikes['Berri 1'].plot()
Out[2]:


#所以我们要创建一个只有 Berri 自行车道的 DataFrame

berri_bikes = bikes[['Berri 1']]
berri_bikes[:5]
Out[3]: 
            Berri 1
Date               
2012-01-01       35
2012-01-02       83
2012-01-03      135
2012-01-04      144
2012-01-05      197

 

接下来， 我们需要添加一列 weekday 。 首先， 我们可以从索引得到星期。 我们还没有谈到索引， 但索引在上面的 DataFrame 中是左边的东西， 在 Date 下面。 它基本上是一年中的所有日子。

berri_bikes.index
Out[4]: 
DatetimeIndex(['2012-01-01', '2012-01-02', '2012-01-03', '2012-01-04',
               '2012-01-05', '2012-01-06', '2012-01-07', '2012-01-08',
               '2012-01-09', '2012-01-10',
               ...
               '2012-10-27', '2012-10-28', '2012-10-29', '2012-10-30',
               '2012-10-31', '2012-11-01', '2012-11-02', '2012-11-03',
               '2012-11-04', '2012-11-05'],
              dtype='datetime64[ns]', name='Date', length=310, freq=None)

 

我们想得到每一行的月份中的日期， 我们可以这样做：
berri_bikes.index.day
Out[6]: 
Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
            ...
            27, 28, 29, 30, 31,  1,  2,  3,  4,  5],
           dtype='int64', name='Date', length=310)

 

我们实际上想要星期
berri_bikes.index.weekday
Out[7]: 
Int64Index([6, 0, 1, 2, 3, 4, 5, 6, 0, 1,
            ...
            5, 6, 0, 1, 2, 3, 4, 5, 6, 0],
           dtype='int64', name='Date', length=310)

 

这是周中的日期， 其中 0 是星期一。 我通过查询日历得到 0 是星期一。
现在我们知道了如何获取星期， 我们可以将其添加到我们的 DataFrame 中作为一列：
berri_bikes['weekday'] = berri_bikes.index.weekday
berri_bikes[:5]
__main__:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[8]: 
            Berri 1  weekday
Date                        
2012-01-01       35        6
2012-01-02       83        0
2012-01-03      135        1
2012-01-04      144        2
2012-01-05      197        3

 

#4.2 按星期统计骑手

 

按星期对行分组， 然后将星期相同的所有值相加”。

weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)
weekday_counts
Out[9]: 
         Berri 1
weekday         
0         134298
1         135305
2         152972
3         160131
4         141771
5         101578
6          99310

weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
weekday_counts
Out[10]: 
           Berri 1
Monday      134298
Tuesday     135305
Wednesday   152972
Thursday    160131
Friday      141771
Saturday    101578
Sunday       99310

 

#4.3完整代码

bikes = pd.read_csv('bikes.csv',
                    sep=';', encoding='latin1',
                    parse_dates=['Date'], dayfirst=True,
                    index_col='Date')
# 添加 weekday 列
berri_bikes = bikes[['Berri 1']]
berri_bikes['weekday'] = berri_bikes.index.weekday
# 按照星期累计骑手， 并绘制出来
weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)
weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
weekday_counts.plot(kind='bar')

 

第五章

 

因为数据原因，跳过

url_template = "http://climate.weather.gc.ca/climateData/bulkdata_e.html?format=csv&stationID=5415&Year={year}&Month={month}&timeframe=1&submit=Download+Data"
url = url_template.format(month=3, year=2012)
weather_mar2012 = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True, encoding='latin1')
weather_mar2012
Traceback (most recent call last):

  File "", line 3, in
    weather_mar2012 = pd.read_csv(url, skiprows=16, index_col='Date/Time', parse_dates=True, encoding='latin1')

  File "E:\anaconda3\lib\site-packages\pandas\io\parsers.py", line 676, in parser_f
    return _read(filepath_or_buffer, kwds)

  File "E:\anaconda3\lib\site-packages\pandas\io\parsers.py", line 431, in _read
    filepath_or_buffer, encoding, compression

  File "E:\anaconda3\lib\site-packages\pandas\io\common.py", line 172, in get_filepath_or_buffer
    req = urlopen(filepath_or_buffer)

  File "E:\anaconda3\lib\site-packages\pandas\io\common.py", line 141, in urlopen
    return urllib.request.urlopen(*args, **kwargs)

  File "E:\anaconda3\lib\urllib\request.py", line 222, in urlopen
    return opener.open(url, data, timeout)

  File "E:\anaconda3\lib\urllib\request.py", line 531, in open
    response = meth(req, response)

  File "E:\anaconda3\lib\urllib\request.py", line 641, in http_response
    'http', request, response, code, msg, hdrs)

  File "E:\anaconda3\lib\urllib\request.py", line 563, in error
    result = self._call_chain(*args)

  File "E:\anaconda3\lib\urllib\request.py", line 503, in _call_chain
    result = func(*args)

  File "E:\anaconda3\lib\urllib\request.py", line 755, in http_error_302
    return self.parent.open(new, timeout=req.timeout)

  File "E:\anaconda3\lib\urllib\request.py", line 531, in open
    response = meth(req, response)

  File "E:\anaconda3\lib\urllib\request.py", line 641, in http_response
    'http', request, response, code, msg, hdrs)

  File "E:\anaconda3\lib\urllib\request.py", line 569, in error
    return self._call_chain(*args)

  File "E:\anaconda3\lib\urllib\request.py", line 503, in _call_chain
    result = func(*args)

  File "E:\anaconda3\lib\urllib\request.py", line 649, in http_error_default
    raise HTTPError(req.full_url, code, msg, hdrs, fp)

HTTPError: Not Found

 

 

第六章

import pandas as pd
weather_2012 = pd.read_csv('weather_2012.csv', parse_dates=True, index_col='Date/Time')
weather_2012[:5]
Out[20]: 
                     Temp (C)  ...               Weather
Date/Time                      ...                      
2012-01-01 00:00:00      -1.8  ...                   Fog
2012-01-01 01:00:00      -1.8  ...                   Fog
2012-01-01 02:00:00      -1.8  ...  Freezing Drizzle,Fog
2012-01-01 03:00:00      -1.5  ...  Freezing Drizzle,Fog
2012-01-01 04:00:00      -1.5  ...                   Fog

[5 rows x 7 columns]

 

#6.1 字符串操作

weather_description = weather_2012['Weather']
is_snowing = weather_description.str.contains('Snow')
is_snowing[:5]
Out[21]: 
Date/Time
2012-01-01 00:00:00    False
2012-01-01 01:00:00    False
2012-01-01 02:00:00    False
2012-01-01 03:00:00    False
2012-01-01 04:00:00    False
Name: Weather, dtype: bool

is_snowing.plot()
Traceback (most recent call last):

  File "", line 1, in
    is_snowing.plot()

  File "E:\anaconda3\lib\site-packages\pandas\plotting\_core.py", line 847, in __call__
    return plot_backend.plot(data, kind=kind, **kwargs)

  File "E:\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\__init__.py", line 61, in plot
    plot_obj.generate()

  File "E:\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py", line 261, in generate
    self._compute_plot_data()

  File "E:\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py", line 410, in _compute_plot_data
    raise TypeError("no numeric data to plot")

TypeError: no numeric data to plot

解决方法：
is_snowing.astype(float).plot()
Out[23]:


 

#6.2 使用 resample 找到下雪最多的月份

import numpy as np

weather_2012['Temp (C)'].resample('M', how=np.median).plot(kind='bar')
Traceback (most recent call last):

  File "", line 1, in
    weather_2012['Temp (C)'].resample('M', how=np.median).plot(kind='bar')

TypeError: resample() got an unexpected keyword argument 'how'

weather_2012['Temp (C)'].resample('M').median().plot(kind='bar')
Out[30]:

毫无奇怪， 七月和八月是最暖和的。

 

然后使用 resample 寻找每个月下雪的时间比例。
is_snowing.astype(float).resample('M').mean()
Out[7]: 
Date/Time
2012-01-31    0.240591
2012-02-29    0.162356
2012-03-31    0.087366
2012-04-30    0.015278
2012-05-31    0.000000
2012-06-30    0.000000
2012-07-31    0.000000
2012-08-31    0.000000
2012-09-30    0.000000
2012-10-31    0.000000
2012-11-30    0.038889
2012-12-31    0.251344
Freq: M, Name: Weather, dtype: float64

is_snowing.astype(float).resample('M').mean().plot(kind='bar')
Out[8]:


 

#6.3 将温度和降雪绘制在一起
temperature = weather_2012['Temp (C)'].resample('M').median()
is_snowing = weather_2012['Weather'].str.contains('Snow')
snowiness = is_snowing.astype(float).resample('M').median()
 

temperature.name = "Temperature"
snowiness.name = "Snowiness"

stats = pd.concat([temperature, snowiness], axis=1)
stats
Out[16]: 
            Temperature  Snowiness
Date/Time                         
2012-01-31        -7.05        0.0
2012-02-29        -4.10        0.0
2012-03-31         2.60        0.0
2012-04-30         6.30        0.0
2012-05-31        16.05        0.0
2012-06-30        19.60        0.0
2012-07-31        22.90        0.0
2012-08-31        22.20        0.0
2012-09-30        16.10        0.0
2012-10-31        11.30        0.0
2012-11-30         1.05        0.0
2012-12-31        -2.85        0.0

stats.plot(kind='bar')
Out[17]:


stats.plot(kind='bar', subplots=True, figsize=(15, 10))
Out[18]: 
array([,
       ],
      dtype=object)