SBT_real_data.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "687bcdbc-642c-4288-8695-9f488980e8ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c30ad72-6fb1-475a-ab56-976b7b854659",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('hackathon-contributions-dataset_v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6a7d71a-0640-4f3c-94de-db43a30aa327",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39828, 55586)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_ids_unique = df.user_id.unique()\n",
    "len(df.user_id.unique()), len(df.address.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7ac1a28-8d78-4cb1-8c70-4f19dabda8d5",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                                                   amount_in_usdt            \\\n",
       "                                                              sum             \n",
       "grant_id                                                     13   15   17     \n",
       "user_id                                                                       \n",
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       "...                                                           ...  ...  ...   \n",
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       "ffff1f4d87eb390fbdeae0ed730e3c3b6dc20d9861e975b...            NaN  NaN  NaN   \n",
       "\n",
       "                                                                             \\\n",
       "                                                                              \n",
       "grant_id                                           20   22   24   25   26     \n",
       "user_id                                                                       \n",
       "00008789d8575f430e7690d361618ff6002d2ccb461c49b...  NaN  NaN  NaN  NaN  NaN   \n",
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       "...                                                 ...  ...  ...  ...  ...   \n",
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       "\n",
       "                                                              ...            \\\n",
       "                                                              ...             \n",
       "grant_id                                           29   36    ... 7951 7953   \n",
       "user_id                                                       ...             \n",
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       "...                                                 ...  ...  ...  ...  ...   \n",
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       "\n",
       "                                                                             \\\n",
       "                                                                              \n",
       "grant_id                                           7954 7956 7967 7974 7976   \n",
       "user_id                                                                       \n",
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       "...                                                 ...  ...  ...  ...  ...   \n",
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       "\n",
       "                                                                   \n",
       "                                                                   \n",
       "grant_id                                           7977 7979 8017  \n",
       "user_id                                                            \n",
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       "...                                                 ...  ...  ...  \n",
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       "\n",
       "[39828 rows x 1501 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method 1\n",
    "new_df = df.groupby(['user_id', 'grant_id']).aggregate({'amount_in_usdt': ['sum']})\n",
    "new_df = new_df.unstack('grant_id')#.fillna(0)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a7c0ebe2-67be-4ab9-ac9a-e44716f9b426",
   "metadata": {},
   "outputs": [
    {
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       "grant_id                                              13    15    17    20    \\\n",
       "user_id                                                                        \n",
       "00008789d8575f430e7690d361618ff6002d2ccb461c49b...     NaN   NaN   NaN   NaN   \n",
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       "0007d499815e212aa805e9e074f230d589c9f937b63db8e...     NaN   NaN   NaN   NaN   \n",
       "...                                                    ...   ...   ...   ...   \n",
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       "ffff1f4d87eb390fbdeae0ed730e3c3b6dc20d9861e975b...     NaN   NaN   NaN   NaN   \n",
       "\n",
       "grant_id                                            22    24    25    26    \\\n",
       "user_id                                                                      \n",
       "00008789d8575f430e7690d361618ff6002d2ccb461c49b...   NaN   NaN   NaN   NaN   \n",
       "00013d2a893bdfa23f936d46e1f1e4d3996197a0d5f7ca3...   NaN   NaN   NaN   NaN   \n",
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       "0007d499815e212aa805e9e074f230d589c9f937b63db8e...   NaN   NaN   NaN   NaN   \n",
       "...                                                  ...   ...   ...   ...   \n",
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       "ffff1f4d87eb390fbdeae0ed730e3c3b6dc20d9861e975b...   NaN   NaN   NaN   NaN   \n",
       "\n",
       "grant_id                                            29    36    ...  7951  \\\n",
       "user_id                                                         ...         \n",
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       "...                                                  ...   ...  ...   ...   \n",
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       "\n",
       "grant_id                                            7953  7954  7956  7967  \\\n",
       "user_id                                                                      \n",
       "00008789d8575f430e7690d361618ff6002d2ccb461c49b...   NaN   NaN   NaN   NaN   \n",
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       "0007d499815e212aa805e9e074f230d589c9f937b63db8e...   NaN   NaN   NaN   NaN   \n",
       "...                                                  ...   ...   ...   ...   \n",
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       "\n",
       "grant_id                                            7974  7976  7977  7979  \\\n",
       "user_id                                                                      \n",
       "00008789d8575f430e7690d361618ff6002d2ccb461c49b...   NaN   NaN   NaN   NaN   \n",
       "00013d2a893bdfa23f936d46e1f1e4d3996197a0d5f7ca3...   NaN   NaN   NaN   NaN   \n",
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       "0007d499815e212aa805e9e074f230d589c9f937b63db8e...   NaN   NaN   NaN   NaN   \n",
       "...                                                  ...   ...   ...   ...   \n",
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       "\n",
       "grant_id                                            8017  \n",
       "user_id                                                   \n",
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       "...                                                  ...  \n",
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       "ffff1f4d87eb390fbdeae0ed730e3c3b6dc20d9861e975b...   NaN  \n",
       "\n",
       "[39828 rows x 1501 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# method 2\n",
    "new_df2 = df.pivot_table(\n",
    "    values='amount_in_usdt', \n",
    "    index=['user_id'], \n",
    "    columns='grant_id', \n",
    "    aggfunc=np.sum)#.fillna(0)\n",
    "new_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "122bcdf5-89e6-4a8e-8d6a-7392abad1077",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check that these two methods yield the same matrix\n",
    "# NOTE: this only works if nan is replaced with zero\n",
    "# np.allclose(new_df, new_df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47583e0f-feef-4fb1-ab25-9f67f3c75f41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the number of projects each user contributed to\n",
    "num_projects_each_user_contributed_to = new_df2.count(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2479b457-c977-494e-bef1-ea68a24c2b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get histogram of number of projects the users contributed to \n",
    "# (truncated to users who contributed to less than 50 projects)\n",
    "num_projects_each_user_contributed_to[num_projects_each_user_contributed_to < 50].hist(bins=100);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}