更改数据框数据类型的明确目的是为了使用该数据进行数据可视化 [英] Changing a dataframes data types for the express purpose of using that data for data visualization
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问题描述
我有一个数据框，其中包含以下类型的列:object，float 64和int64.我想将这些类型更改为可用于数据可视化的类型.我已经尝试过使用astype方法，但出现错误. 我该如何将这些类型更改为更适合数据可视化的一种.
I have a data frame that has columns within it that are of the types: object, float 64 and int64. I want to change these types to one that I can use for data visualisations. I have tried already by using the astype method but I am getting an error. How do I go about changing these types to one more suited for data visualization.
#Import data into a dataframe
raw_data = pd.read_csv('FuelPrices2016 -2019 ulsp.csv')
raw_data.head()
#Import data into a dataframe
raw_data = pd.read_csv('FuelPrices2016 -2019 ulsp.csv')
raw_data.head()
Date Pump price in pence/litre ULSP Duty rate in pence/litre/ULSP VAT percentage rate Unnamed: 4
0 02/01/2012 132.40 57.95 20 NaN
1 09/01/2012 132.68 57.95 20 NaN
2 16/01/2012 133.29 57.95 20 NaN
3 23/01/2012 133.72 57.95 20 NaN
4 30/01/2012 134.10 57.95 20 NaN
#Drop unnamed column
raw_b = raw_data.drop(columns=['Unnamed: 4',])
raw_b
Date Pump price in pence/litre ULSP Duty rate in pence/litre/ULSP VAT percentage rate
0 02/01/2012 132.40 57.95 20
1 09/01/2012 132.68 57.95 20
2 16/01/2012 133.29 57.95 20
3 23/01/2012 133.72 57.95 20
4 30/01/2012 134.10 57.95 20
... ... ... ... ...
396 05/08/2019 128.37 57.95 20
397 12/08/2019 128.36 57.95 20
398 19/08/2019 128.17 57.95 20
399 26/08/2019 128.22 57.95 20
400 02/09/2019 127.86 57.95 20
401 rows × 4 columns
#Describe the data
raw_b.describe()
Pump price in pence/litre ULSP Duty rate in pence/litre/ULSP VAT percentage rate
count 401.000000 4.010000e+02 401.0
mean 123.043840 5.795000e+01 20.0
std 10.175522 7.114304e-15 0.0
min 101.360000 5.795000e+01 20.0
25% 115.600000 5.795000e+01 20.0
50% 123.270000 5.795000e+01 20.0
75% 130.830000 5.795000e+01 20.0
max 142.170000 5.795000e+01 20.0
#Check the types of the columns
raw_b.dtypes
Date object
Pump price in pence/litre ULSP float64
Duty rate in pence/litre/ULSP float64
VAT percentage rate int64
dtype: object
#Change date into a date
raw_b(pd.to_datetime(['Date']))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
~\Anaconda3\envs\py3-TF2.0\lib\site-packages\pandas\core\arrays\datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1978 try:
-> 1979 values, tz_parsed = conversion.datetime_to_datetime64(data)
1980 # If tzaware, these values represent unix timestamps, so we
pandas\_libs\tslibs\conversion.pyx in pandas._libs.tslibs.conversion.datetime_to_datetime64()
TypeError: Unrecognized value type: <class 'str'>
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-22-5ab1ccfbf9a4> in <module>
1 #Change date into a date
----> 2 raw_b(pd.to_datetime(['Date']))
~\Anaconda3\envs\py3-TF2.0\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
206 else:
207 kwargs[new_arg_name] = new_arg_value
--> 208 return func(*args, **kwargs)
209
210 return wrapper
~\Anaconda3\envs\py3-TF2.0\lib\site-packages\pandas\core\tools\datetimes.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, infer_datetime_format, origin, cache)
792 result = _convert_and_box_cache(arg, cache_array, box)
793 else:
--> 794 result = convert_listlike(arg, box, format)
795 else:
796 result = convert_listlike(np.array([arg]), box, format)[0]
~\Anaconda3\envs\py3-TF2.0\lib\site-packages\pandas\core\tools\datetimes.py in _convert_listlike_datetimes(arg, box, format, name, tz, unit, errors, infer_datetime_format, dayfirst, yearfirst, exact)
461 errors=errors,
462 require_iso8601=require_iso8601,
--> 463 allow_object=True,
464 )
465
~\Anaconda3\envs\py3-TF2.0\lib\site-packages\pandas\core\arrays\datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1982 return values.view("i8"), tz_parsed
1983 except (ValueError, TypeError):
-> 1984 raise e
1985
1986 if tz_parsed is not None:
~\Anaconda3\envs\py3-TF2.0\lib\site-packages\pandas\core\arrays\datetimes.py in objects_to_datetime64ns(data, dayfirst, yearfirst, utc, errors, require_iso8601, allow_object)
1973 dayfirst=dayfirst,
1974 yearfirst=yearfirst,
-> 1975 require_iso8601=require_iso8601,
1976 )
1977 except ValueError as e:
pandas\_libs\tslib.pyx in pandas._libs.tslib.array_to_datetime()
pandas\_libs\tslib.pyx in pandas._libs.tslib.array_to_datetime()
pandas\_libs\tslib.pyx in pandas._libs.tslib.array_to_datetime_object()
pandas\_libs\tslib.pyx in pandas._libs.tslib.array_to_datetime_object()
pandas\_libs\tslibs\parsing.pyx in pandas._libs.tslibs.parsing.parse_datetime_string()
~\Anaconda3\envs\py3-TF2.0\lib\site-packages\dateutil\parser\_parser.py in parse(timestr, parserinfo, **kwargs)
1356 return parser(parserinfo).parse(timestr, **kwargs)
1357 else:
-> 1358 return DEFAULTPARSER.parse(timestr, **kwargs)
1359
1360
~\Anaconda3\envs\py3-TF2.0\lib\site-packages\dateutil\parser\_parser.py in parse(self, timestr, default, ignoretz, tzinfos, **kwargs)
647
648 if res is None:
--> 649 raise ValueError("Unknown string format:", timestr)
650
651 if len(res) == 0:
ValueError: ('Unknown string format:', 'Date')
推荐答案
对象的格式正确，您可以尝试使用以下方法将日期"列设置为日期时间类型:
The format for the objects are correct, you can try setting the 'Date' column to a datetime type by using:
raw_b['Date'] = pd.to_datetime(raw_b['Date'])
如果这不起作用，则应使用以下格式传递格式:
If that doesn't work, you should pass the format by using:
raw_b['Date'] = pd.to_datetime(raw_b['Date'], format= '%d/%m/%Y')
然后使用此数据绘制图形.
下一步是将Date作为x轴传递到绘图，或将其设置为数据框中的索引(因为当日期在索引中时，某些功能更容易应用):
And then use this data to plot your graph.
Your next step would be to either pass the Date as x-axis to your plot, or to set it as index in your dataframe (because some functions are easier applied when the dates are in the index):
raw_b = raw_b.set_index('Date')
这是代码:
import pandas as pd
import matplotlib.pyplot as plt
a = {'Date':['02/01/2012','09/01/2012','16/01/2012','23/01/2012','30/01/2012'],'Pump price in pence/litre ULSP':[132.40,132.68,133.29,133.72,134.10],'Duty rate in pence/litre/ULSP':[57.95,57.95,57.95,57.95,57.95],'VAT percentage rate':[20,20,20,20,20]}
raw_b = pd.DataFrame(a)
print(raw_b)
print(raw_b.dtypes)
raw_b['Date'] = pd.to_datetime(raw_b['Date'],format='%d/%m/%Y')
print(raw_b.dtypes)
print(raw_b)
raw_b = raw_b.set_index('Date')
print(raw_b)
raw_b.plot.line()
plt.show()
这是第一张印刷品(它的原始外观)以及以下类型:
This is the first print (how it originally looks like) together with the types:
Date Pump price in pence/litre ULSP Duty rate in pence/litre/ULSP VAT percentage rate
0 02/01/2012 132.40 57.95 20
1 09/01/2012 132.68 57.95 20
2 16/01/2012 133.29 57.95 20
3 23/01/2012 133.72 57.95 20
4 30/01/2012 134.10 57.95 20
Date object
Pump price in pence/litre ULSP float64
Duty rate in pence/litre/ULSP float64
VAT percentage rate int64
dtype: object
应用pd.to_datetime并传递正确的格式后:
After applying pd.to_datetime and passing the correct format:
Date datetime64[ns]
Pump price in pence/litre ULSP float64
Duty rate in pence/litre/ULSP float64
VAT percentage rate int64
dtype: object
这是它的样子:
Date Pump price in pence/litre ULSP Duty rate in pence/litre/ULSP VAT percentage rate
0 2012-01-02 132.40 57.95 20
1 2012-01-09 132.68 57.95 20
2 2012-01-16 133.29 57.95 20
3 2012-01-23 133.72 57.95 20
4 2012-01-30 134.10 57.95 20
最后，将Date设置为索引后:
Finally, after setting Date as index:
Pump price in pence/litre ULSP Duty rate in pence/litre/ULSP VAT percentage rate
Date
2012-01-02 132.40 57.95 20
2012-01-09 132.68 57.95 20
2012-01-16 133.29 57.95 20
2012-01-23 133.72 57.95 20
2012-01-30 134.10 57.95 20
最后创建图并显示它之后，这是输出:
Finally after creatiing the plot and showing it, this is the output:
这篇关于更改数据框数据类型的明确目的是为了使用该数据进行数据可视化的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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