从具有不同值和类型的一列创建新的数据框列

Question

我正在尝试按鱼类名称创建新列并将整数作为值，保持索引以在之后进行数据框连接。

import pandas as pd

df = pd.read_csv("fishCounts.csv",index_col=0)

countsdf = df[["Fish Count"]].copy()

countsdf.head()

    

Fish Count

0   38 Sand Bass, 16 Sculpin, 10 Blacksmith

1   138 Sculpin, 28 Sand Bass

2   150 Sculpin Released, 102 Sculpin, 40 Sanddab

3   156 Sculpin, 29 Sand Bass, 5 Black Croaker, 3 ...

4   161 Sculpin

countsdf.columns = ["fish"]

countsdf.fish = countsdf.fish.str.split(", ", expand=False)

countsdf.head()

fish

0   [38 Sand Bass, 16 Sculpin, 10 Blacksmith]

1   [138 Sculpin, 28 Sand Bass]

2   [150 Sculpin Released, 102 Sculpin, 40 Sanddab]

3   [156 Sculpin, 29 Sand Bass, 5 Black Croaker, 3...

4   [161 Sculpin]

这是我不确定去哪里的地方。遍历数据框行？列出字典？我可以以不同的方式导入数据以使这更容易吗？

编辑：这就是我想要达到的目的。

  Sand Bass   Sculpin   Blacksmith   Sculpin Released  Sanddab  Black Croaker

0        38        16           10

1        28        138

2                  102                            150       40

3        29        156                                                      5

4                  161

Answer 1

类似于@Manakin的东西转Fish Countint列表df['Fish Count']=df['Fish Count'].str.split(',')分解以将每条鱼与它的 id 分开df2=df.explode('Fish Count')创建字典。Fish Count在这里，我使用列表推导式在将值拆分为数字后的空格后派生键和值{i:j for i,j in df2['Fish Count'].str.split(r'(?<=\d)\s')}结果{'38': 'Sand Bass',&nbsp;' 16': 'Sculpin',&nbsp;' 10': 'Blacksmith',&nbsp;'138': 'Sculpin',&nbsp;' 28': 'Sand Bass',&nbsp;'150': 'Sculpin Released',&nbsp;' 102': 'Sculpin',&nbsp;' 40': 'Sanddab',&nbsp;'156': 'Sculpin',&nbsp;' 29': 'Sand Bass',&nbsp;' 5': 'Black Croaker',&nbsp;'161': 'Sculpin'}如果需要可以打印print(pd.DataFrame.from_dict({i:j for i,j in df2['Fish Count'].str.split(r'(?<=\d)\s')}, orient='index'))&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;038&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sand Bass&nbsp;16&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp;10&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Blacksmith138&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp;28&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass150&nbsp; &nbsp;Sculpin Released&nbsp;102&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sculpin&nbsp;40&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sanddab156&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp;29&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass&nbsp;5&nbsp; &nbsp; &nbsp; &nbsp;Black Croaker161&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin

Answer 2

IIUC，我们可以使用str.splitand str.extractwithstacks = df['Fish Count'].str.split(',',expand=True).stack()s.str.extract('(\d+)(\D+)')产量 -&nbsp; &nbsp; &nbsp; &nbsp;0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 10 0&nbsp; &nbsp;38&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass&nbsp; 1&nbsp; &nbsp;16&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; 2&nbsp; &nbsp;10&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Blacksmith1 0&nbsp; 138&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; 1&nbsp; &nbsp;28&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass2 0&nbsp; 150&nbsp; &nbsp;Sculpin Released&nbsp; 1&nbsp; 102&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; 2&nbsp; &nbsp;40&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sanddab3 0&nbsp; 156&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; 1&nbsp; &nbsp;29&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass&nbsp; 2&nbsp; &nbsp; 5&nbsp; &nbsp; &nbsp; Black Croaker&nbsp; 3&nbsp; &nbsp; 3&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ...4 0&nbsp; 161&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin然后由您决定您想要/需要的格式。IEs.str.extract('(\d+)(\D+)').groupby(level=[1]).agg(list)&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 10&nbsp; [38, 138, 150, 156, 161]&nbsp; [ Sand Bass,&nbsp; Sculpin,&nbsp; Sculpin Released,&nbsp; Scu...1&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[16, 28, 102, 29]&nbsp; &nbsp; &nbsp; &nbsp;[ Sculpin,&nbsp; Sand Bass,&nbsp; Sculpin,&nbsp; Sand Bass]2&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[10, 40, 5]&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; [ Blacksmith,&nbsp; Sanddab,&nbsp; Black Croaker]3&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[3]&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[ ...]或者s.str.extract('(\d+)(\D+)').unstack(1)&nbsp; &nbsp; &nbsp;0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;1&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;&nbsp; &nbsp; &nbsp;0&nbsp; &nbsp; 1&nbsp; &nbsp; 2&nbsp; &nbsp; 3&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;1&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;2&nbsp; &nbsp; &nbsp;30&nbsp; &nbsp;38&nbsp; &nbsp;16&nbsp; &nbsp;10&nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass&nbsp; &nbsp; &nbsp;Sculpin&nbsp; &nbsp; &nbsp; Blacksmith&nbsp; &nbsp;NaN1&nbsp; 138&nbsp; &nbsp;28&nbsp; NaN&nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; &nbsp;Sand Bass&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp;NaN2&nbsp; 150&nbsp; 102&nbsp; &nbsp;40&nbsp; NaN&nbsp; &nbsp;Sculpin Released&nbsp; &nbsp; &nbsp;Sculpin&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sanddab&nbsp; &nbsp;NaN3&nbsp; 156&nbsp; &nbsp;29&nbsp; &nbsp; 5&nbsp; &nbsp; 3&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; &nbsp;Sand Bass&nbsp; &nbsp;Black Croaker&nbsp; &nbsp;...4&nbsp; 161&nbsp; NaN&nbsp; NaN&nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp;NaN或者s.str.extract('(\d+)(\D+)').valuesarray([['38', ' Sand Bass'],&nbsp; &nbsp; &nbsp; &nbsp;['16', ' Sculpin'],&nbsp; &nbsp; &nbsp; &nbsp;['10', ' Blacksmith'],&nbsp; &nbsp; &nbsp; &nbsp;['138', ' Sculpin'],&nbsp; &nbsp; &nbsp; &nbsp;['28', ' Sand Bass'],&nbsp; &nbsp; &nbsp; &nbsp;['150', ' Sculpin Released'],&nbsp; &nbsp; &nbsp; &nbsp;['102', ' Sculpin'],&nbsp; &nbsp; &nbsp; &nbsp;['40', ' Sanddab'],&nbsp; &nbsp; &nbsp; &nbsp;['156', ' Sculpin'],&nbsp; &nbsp; &nbsp; &nbsp;['29', ' Sand Bass'],&nbsp; &nbsp; &nbsp; &nbsp;['5', ' Black Croaker'],&nbsp; &nbsp; &nbsp; &nbsp;['3', ' ...'],&nbsp; &nbsp; &nbsp; &nbsp;['161', ' Sculpin']], dtype=object)你可以把它变成一个字典。# actually i'd use fish : num -&nbsp;# sorry closed my ide keys can only be unique in a dict.{num : fish for num, fish in s.str.extract('(\d+)(\D+)').values}{'38': ' Sand Bass',&nbsp;'16': ' Sculpin',&nbsp;'10': ' Blacksmith',&nbsp;'138': ' Sculpin',&nbsp;'28': ' Sand Bass',&nbsp;'150': ' Sculpin Released',&nbsp;'102': ' Sculpin',&nbsp;'40': ' Sanddab',&nbsp;'156': ' Sculpin',&nbsp;'29': ' Sand Bass',&nbsp;'5': ' Black Croaker',&nbsp;'3': ' ...',&nbsp;'161': ' Sculpin'}

Answer 3

首先，您需要展开您制作的列表，然后您可以使用 extract with regex 两次，一次匹配数字，然后匹配文本。有了数据data = '38 Sand Bass, 16 Sculpin, 10 Blacksmith\n138 Sculpin, 28 Sand Bass\n150 Sculpin Released, 102 Sculpin, 40 Sanddab\n156 Sculpin, 29 Sand Bass, 5 Black Croaker\n161 Sculpin'df = pd.DataFrame(data.split('\n'), columns=['Fish Count'])做countsdf = df['Fish Count'].str.split(', ')countsdf = countsdf.explode('Fish Count').rename('fish').to_frame()countsdf['count'] = countsdf.fish.str.extract('([0-9]+)')countsdf['species'] = countsdf.fish.str.extract('([a-zA-Z]+[ a-zA-Z]*)')countsdf.drop('fish', axis=1, inplace=True)输出&nbsp; &nbsp;count&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;species0&nbsp; &nbsp; &nbsp;38&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sand Bass1&nbsp; &nbsp; &nbsp;16&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sculpin2&nbsp; &nbsp; &nbsp;10&nbsp; &nbsp; &nbsp; &nbsp; Blacksmith3&nbsp; &nbsp; 138&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sculpin4&nbsp; &nbsp; &nbsp;28&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sand Bass5&nbsp; &nbsp; 150&nbsp; Sculpin Released6&nbsp; &nbsp; 102&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sculpin7&nbsp; &nbsp; &nbsp;40&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sanddab8&nbsp; &nbsp; 156&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sculpin9&nbsp; &nbsp; &nbsp;29&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sand Bass10&nbsp; &nbsp; &nbsp;5&nbsp; &nbsp; &nbsp;Black Croaker11&nbsp; &nbsp;161&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sculpin

Answer 4

使用@Manakin 的回答来访问这个多索引数据框：&nbsp; &nbsp; &nbsp; &nbsp;0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 10 0&nbsp; &nbsp;38&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass&nbsp; 1&nbsp; &nbsp;16&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; 2&nbsp; &nbsp;10&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Blacksmith1 0&nbsp; 138&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; 1&nbsp; &nbsp;28&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass2 0&nbsp; 150&nbsp; &nbsp;Sculpin Released&nbsp; 1&nbsp; 102&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; 2&nbsp; &nbsp;40&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sanddab3 0&nbsp; 156&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin&nbsp; 1&nbsp; &nbsp;29&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sand Bass&nbsp; 2&nbsp; &nbsp; 5&nbsp; &nbsp; &nbsp; Black Croaker4 0&nbsp; 161&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; Sculpin然后，我重命名了列，去除了“物种”的前导和结尾空白，切换了列顺序，并设置了索引名称。s.columns = ['num','species']s.species = s.species.str.strip()s = s.reindex(['species','num'],axis=1)s.index.names = ['a','b']s.head()&nbsp; &nbsp; &nbsp; &nbsp; species&nbsp; &nbsp; &nbsp;numa&nbsp; &nbsp;b&nbsp; &nbsp; &nbsp; &nbsp;0&nbsp; &nbsp;0&nbsp; &nbsp;Sand Bass&nbsp; &nbsp;381&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;Sculpin&nbsp; &nbsp;162&nbsp; &nbsp; &nbsp; Blacksmith&nbsp; &nbsp;101&nbsp; &nbsp;0&nbsp; &nbsp; &nbsp;Sculpin&nbsp; &nbsp;1381&nbsp; &nbsp; &nbsp; &nbsp;Sand Bass&nbsp; &nbsp;28然后我展平并重置索引，并删除 b 索引。s_flat = s.reset_index()s_reindexed = s_flat.set_index(['a','species'])s_reindexed = s_reindexed.drop(columns='b')s_reindexed.head()&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;numa&nbsp; &nbsp;species&nbsp; &nbsp; &nbsp;0 Sand Bass&nbsp; &nbsp; &nbsp;38&nbsp; &nbsp; &nbsp;Sculpin&nbsp; &nbsp; 16&nbsp; Blacksmith&nbsp; &nbsp; 101&nbsp; &nbsp; Sculpin&nbsp; &nbsp; 138&nbsp; &nbsp;Sand Bass&nbsp; &nbsp; 28最后，我取消堆叠并删除了柱状多索引级别。我有一个 Null 列，我也必须删除s_reindexed = s_reindexed.unstack(1)s_reindexed.columns = s_reindexed.columns.droplevel(0)s_reset = s_reindexed.drop(columns=np.nan)s_reset .head()species&nbsp; &nbsp; &nbsp;Albacore&nbsp; &nbsp; Barracuda&nbsp; &nbsp;Barracuda Released&nbsp; Bat Ray Released&nbsp; &nbsp; Black Croaker&nbsp; &nbsp;Black Seabass Released&nbsp; Blacksmith&nbsp; Blue Perch&nbsp; Bluefin Tuna&nbsp; &nbsp; Bocaccio ...a&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 10&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN ...1&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN ...2&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN ...3&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 5&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;3&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN ...4&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;NaN ...