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在将新列添加到pandas DataFrame时引发TypeError

[英]Raise TypeError when adding new column to a pandas DataFrame

I've been watching an online course about data analysis using Python. 我一直在观看有关使用Python进行数据分析的在线课程。 I came across a problem when following exactly what the instructor did. 当我完全按照指导老师的做事时遇到一个问题。 Basically, I pulled a data frame called "flights" from seaborn and set the index "year" and "month" and unstacked it. 基本上,我从seaborn提取了一个名为“航班”的数据框,并设置了索引“ year”和“ month”并进行了堆叠。 The following codes are used: 使用以下代码:

import seaborn
import pandas as pd
flights = seaborn.load_dataset("flights")
flights_indexed = flights.set_index(["year","month"])
flights_unstacked = flights_indexed.unstack()
flights_unstacked

the final data frame is like this 最终的数据帧是这样的

Then I am trying to add a new column called "Total" at the end for the sum of each year using the following code: 然后,我尝试使用以下代码在每年的末尾添加一个名为“总计”的新列:

flights_unstacked["passengers"]["Total"] = flights_unstacked.sum(axis = 1)

But it raised a TypeError: cannot insert an item into a CategoricalIndex that is not already an existing category. 但是它引发了TypeError: cannot insert an item into a CategoricalIndex that is not already an existing category.

I am new to data manipulation using pandas. 我是使用熊猫进行数据处理的新手。 Anyone can tell me how to fix this? 有人可以告诉我如何解决此问题吗? Is this a version issue, because the online instructor did exactly the same thing but his works just fine. 这是一个版本问题,因为在线讲师做了完全一样的事情,但是他的工作还不错。 PS: I use Python 2.7 and pandas 0.20.3. PS:我使用Python 2.7和pandas 0.20.3。

The seaborn.load_dataset line detects the month column as a category data type. seaborn.load_dataset行将month列检测为category数据类型。 To get around this error, cast categorical to str with this line right after flights = seaborn.load_dataset("flights") : 要解决此错误,请在flights = seaborn.load_dataset("flights")之后立即将以下categorical flights = seaborn.load_dataset("flights")str

flights["month"] = flights["month"].astype(str)

To sort the month strings in chronological order, first drop the top level (level=0) of the columns of flights_unstacked (this level holds the single value passengers ): 要按时间顺序对月份字符串进行排序,请首先放下flights_unstacked列的最高级别(级别= 0)(此级别包含单个值passengers ):

import seaborn
import pandas as pd

flights = seaborn.load_dataset("flights")
flights["month"] = flights["month"].astype(str)

flights_indexed = flights.set_index(["year", "month"])
flights_unstacked = flights_indexed.unstack()
flights_unstacked.columns = flights_unstacked.columns.droplevel(0)

Then reindex the month-string columns according to a list of month strings that you pre-built in chronological order: 然后根据您按时间顺序预先构建的月份字符串列表重新索引月份字符串列:

import calendar
months = [calendar.month_name[i] for i in range(1, 13)]
flights_unstacked = flights_unstacked[months]

Finally, you can add a column of totals: 最后,您可以添加总计列:

flights_unstacked["Total"] = flights_unstacked.sum(axis=1)

Result: 结果:

In [329]: flights_unstacked
Out[329]:
month  January  February  March  April  May  June  July  August  September  October  November  December  Total
year                                                                                                          
1949       112       118    132    129  121   135   148     148        136      119       104       118   1520
1950       115       126    141    135  125   149   170     170        158      133       114       140   1676
1951       145       150    178    163  172   178   199     199        184      162       146       166   2042
1952       171       180    193    181  183   218   230     242        209      191       172       194   2364
1953       196       196    236    235  229   243   264     272        237      211       180       201   2700
1954       204       188    235    227  234   264   302     293        259      229       203       229   2867
1955       242       233    267    269  270   315   364     347        312      274       237       278   3408
1956       284       277    317    313  318   374   413     405        355      306       271       306   3939
1957       315       301    356    348  355   422   465     467        404      347       305       336   4421
1958       340       318    362    348  363   435   491     505        404      359       310       337   4572
1959       360       342    406    396  420   472   548     559        463      407       362       405   5140
1960       417       391    419    461  472   535   622     606        508      461       390       432   5714

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