Section 6-26 Python for Data Analysis - Pandas Series.ipynb

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    "import numpy as np\n",
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    "labels = ['a', 'b', 'c'] # list\n",
    "my_data = [10, 20, 30] # data\n",
    "arr = np.array(my_data) # array\n",
    "d = {'a': 10, 'b': 20, 'c': 30} #dictionary"
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       "<pre>0    10\n",
       "1    20\n",
       "2    30\n",
       "dtype: int64<\/pre>"
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    "# labels index series\n",
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       "<pre>a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64<\/pre>"
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    "pd.Series(my_data, labels)"
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       "<pre>a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64<\/pre>"
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       "<pre>0    10\n",
       "1    20\n",
       "2    30\n",
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    "pd.Series(arr, labels)"
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   "execution_count":33,
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       "<pre>a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64<\/pre>"
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    "labels"
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   "execution_count":35,
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      "text\/plain":[
       "['a', 'b', 'c']"
      ]
     },
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    "pd.Series(data=[sum, print, len])"
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   "execution_count":36,
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       "<pre>0      %lt;built-in function sum%gt;\n",
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    "ser1 = pd.Series([1,2,3,4], ['USA', 'Germany', 'USSR', 'Japan'])"
   ],
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       "<pre>USA        1\n",
       "Germany    2\n",
       "USSR       3\n",
       "Japan      4\n",
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    "ser2 = pd.Series([1,2,5,4], ['USA', 'Germany', 'Italy', 'Japan'])"
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       "Japan      4\n",
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       "<pre>Germany    4.0\n",
       "Italy      NaN\n",
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