Code/Spark_EDA_TSTests.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spark configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%configure -f\n",
    "{\n",
    "    \"conf\": {\n",
    "        \"spark.pyspark.python\": \"python3\",\n",
    "        \"spark.pyspark.virtualenv.enabled\": \"true\",\n",
    "        \"spark.pyspark.virtualenv.type\":\"native\",\n",
    "        \"spark.pyspark.virtualenv.bin.path\":\"/usr/bin/virtualenv\"\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Spark application\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>1</td><td>application_1684737042965_0003</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://ip-172-31-81-102.ec2.internal:20888/proxy/application_1684737042965_0003/\">Link</a></td><td><a target=\"_blank\" href=\"http://ip-172-31-89-159.ec2.internal:8042/node/containerlogs/container_1684737042965_0003_01_000001/livy\">Link</a></td><td>✔</td></tr></table>"
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     "text": [
      "SparkSession available as 'spark'.\n"
     ]
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     "name": "stdout",
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     "text": [
      "'3.0.1-amzn-0'"
     ]
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   "source": [
    "spark.version"
   ]
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   "cell_type": "code",
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     "text": [
      "Package                    Version   \n",
      "-------------------------- ----------\n",
      "beautifulsoup4             4.9.3     \n",
      "boto                       2.49.0    \n",
      "click                      7.1.2     \n",
      "jmespath                   0.10.0    \n",
      "joblib                     0.17.0    \n",
      "lxml                       4.6.1     \n",
      "mysqlclient                1.4.2     \n",
      "nltk                       3.5       \n",
      "nose                       1.3.4     \n",
      "numpy                      1.16.5    \n",
      "pip                        9.0.1     \n",
      "py-dateutil                2.2       \n",
      "python37-sagemaker-pyspark 1.4.1     \n",
      "pytz                       2020.1    \n",
      "PyYAML                     5.3.1     \n",
      "regex                      2020.10.28\n",
      "setuptools                 28.8.0    \n",
      "six                        1.13.0    \n",
      "tqdm                       4.51.0    \n",
      "wheel                      0.29.0    \n",
      "windmill                   1.6"
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   ],
   "source": [
    "sc.list_packages()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# install packages\n",
    "sc.install_pypi_package(\"pandas==1.0.5\")\n",
    "sc.install_pypi_package(\"matplotlib==3.2.1\")\n",
    "sc.install_pypi_package(\"pybind11\")\n",
    "sc.install_pypi_package(\"scipy==1.2.1\")\n",
    "sc.install_pypi_package(\"statsmodels==0.12.0\")\n",
    "sc.install_pypi_package(\"boto3==1.18.21\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load transactions data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see block-level data directly, query the 'blocks' table (code below). However this table does not include information on the aggregate transaction fees in the block, only gas units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "text": [
      "'\\nfrom pyspark.sql.functions import col\\n\\nblocks = spark.read.parquet(\"s3://aws-public-blockchain/v1.0/eth/blocks/\")                     .where(col(\\'date\\').between(start_date, end_date))\\n'"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "from pyspark.sql.functions import col\n",
    "\n",
    "blocks = spark.read.parquet(\"s3://aws-public-blockchain/v1.0/eth/blocks/\") \\\n",
    "                    .where(col('date').between(start_date, end_date))\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So instead, I query the 'transactions' table and then aggregate into block level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "from pyspark.sql.functions import col\n",
    "\n",
    "start_date = '2021-09-01'\n",
    "end_date = '2023-05-01'\n",
    "raw = spark.read.parquet(\"s3://aws-public-blockchain/v1.0/eth/transactions/\") \\\n",
    "                    .where(col('date').between(start_date, end_date))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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     "text": [
      "+------+--------------------+--------------------+-----+-----------+------------+--------------------+-----------------+--------------------+--------------------+-----------+---------------------------+----------------+------------------------+--------------+---------------------------+----------------+---------------+------------------------+-------------------+----------+--------------------+\n",
      "|   gas|                hash|               input|nonce|      value|block_number|          block_hash|transaction_index|        from_address|          to_address|  gas_price|receipt_cumulative_gas_used|receipt_gas_used|receipt_contract_address|receipt_status|receipt_effective_gas_price|transaction_type|max_fee_per_gas|max_priority_fee_per_gas|    block_timestamp|      date|       last_modified|\n",
      "+------+--------------------+--------------------+-----+-----------+------------+--------------------+-----------------+--------------------+--------------------+-----------+---------------------------+----------------+------------------------+--------------+---------------------------+----------------+---------------+------------------------+-------------------+----------+--------------------+\n",
      "| 60000|0xbfe1a445e748cea...|0xa9059cbb0000000...| 4026|        0.0|    16518105|0xbc8fbb9b2aa20f7...|                0|0x4e28710830e2c91...|0xf8ad7dfe656188a...|60000000000|                      58824|           58824|                        |             1|                60000000000|               0|              0|                       0|2023-01-30 07:19:47|2023-01-30|2023-01-31 00:34:...|\n",
      "| 80000|0xd64a068992d578b...|0x574da7170000000...|   65|3.276354E16|    16518105|0xbc8fbb9b2aa20f7...|                1|0x0ed691cfbd87c4b...|0xd37bbe5744d730a...|30000000000|                      98660|           39836|                        |             1|                30000000000|               0|              0|                       0|2023-01-30 07:19:47|2023-01-30|2023-01-31 00:34:...|\n",
      "|101440|0xa2e467191648933...|0xa9059cbb0000000...| 3767|        0.0|    16518105|0xbc8fbb9b2aa20f7...|                2|0x556ef0e9c40dda7...|0xdac17f958d2ee52...|21600000000|                     144769|           46109|                        |             1|                21600000000|               0|              0|                       0|2023-01-30 07:19:47|2023-01-30|2023-01-31 00:34:...|\n",
      "|219514|0x5a0ac86debda3b4...|0x0dcd7a6c0000000...|70621|        0.0|    16518105|0xbc8fbb9b2aa20f7...|                3|0xb02c6c40a798184...|0x3a5cc8689d1b0ce...|20726344000|                     263126|          118357|                        |             1|                20726344000|               0|              0|                       0|2023-01-30 07:19:47|2023-01-30|2023-01-31 00:34:...|\n",
      "|850000|0x9d9a3d911488516...|0x7ff36ab50000000...|   20|     5.0E16|    16518105|0xbc8fbb9b2aa20f7...|                4|0x954eb1ca083afa9...|0x7a250d5630b4cf5...|20229607745|                     467323|          204197|                        |             1|                20229607745|               2|    22540334580|              6000000000|2023-01-30 07:19:47|2023-01-30|2023-01-31 00:34:...|\n",
      "+------+--------------------+--------------------+-----+-----------+------------+--------------------+-----------------+--------------------+--------------------+-----------+---------------------------+----------------+------------------------+--------------+---------------------------+----------------+---------------+------------------------+-------------------+----------+--------------------+\n",
      "only showing top 5 rows"
     ]
    }
   ],
   "source": [
    "raw.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
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   "outputs": [
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     "text": [
      "+----------+--------------------+-------+-----------+---------------------------+----------------+---------------------------+\n",
      "|      date|                hash|    gas|  gas_price|receipt_cumulative_gas_used|receipt_gas_used|receipt_effective_gas_price|\n",
      "+----------+--------------------+-------+-----------+---------------------------+----------------+---------------------------+\n",
      "|2023-01-30|0xbfe1a445e748cea...|  60000|60000000000|                      58824|           58824|                60000000000|\n",
      "|2023-01-30|0xd64a068992d578b...|  80000|30000000000|                      98660|           39836|                30000000000|\n",
      "|2023-01-30|0xa2e467191648933...| 101440|21600000000|                     144769|           46109|                21600000000|\n",
      "|2023-01-30|0x5a0ac86debda3b4...| 219514|20726344000|                     263126|          118357|                20726344000|\n",
      "|2023-01-30|0x9d9a3d911488516...| 850000|20229607745|                     467323|          204197|                20229607745|\n",
      "|2023-01-30|0x891cc7766e6188e...|3435715|20000000000|                    3903038|         3435715|                20000000000|\n",
      "|2023-01-30|0x7d6fd5f03ae86b3...|1892903|19229607745|                    4030707|          127669|                19229607745|\n",
      "|2023-01-30|0x3e4fd6fee597891...|1821670|19229607745|                    4147960|          117253|                19229607745|\n",
      "|2023-01-30|0x28e8cfface36978...|1929531|19229607745|                    4265213|          117253|                19229607745|\n",
      "|2023-01-30|0x149bb99b15e619c...|1925023|19229607745|                    4382466|          117253|                19229607745|\n",
      "+----------+--------------------+-------+-----------+---------------------------+----------------+---------------------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "# columns related to gas\n",
    "raw.select(['date','hash','gas', 'gas_price', \n",
    "             'receipt_cumulative_gas_used','receipt_gas_used',\n",
    "             'receipt_effective_gas_price']).show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above are all fields related to transaction fee (gas fee).  \n",
    "Ethereum gas fees are computed using gas units * gas price. Prices are in wei/unit and 1 ETH = 10^18 wei.  \n",
    "gas, gas_price: values when transaction was initiated  \n",
    "receipt_gas_used, receipt_effective_gas_price: values calculated when settling transaction  \n",
    "I will use the second pair for calculating transaction fee ex-post. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transaction fee feature / Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
      "+----------+--------------------+--------------------+\n",
      "|      date|                hash|             txn_fee|\n",
      "+----------+--------------------+--------------------+\n",
      "|2023-01-30|0xbfe1a445e748cea...|          0.00352944|\n",
      "|2023-01-30|0xd64a068992d578b...|          0.00119508|\n",
      "|2023-01-30|0xa2e467191648933...|         9.959544E-4|\n",
      "|2023-01-30|0x5a0ac86debda3b4...|   0.002453107896808|\n",
      "|2023-01-30|0x9d9a3d911488516...|0.004130825212705765|\n",
      "|2023-01-30|0x891cc7766e6188e...|           0.0687143|\n",
      "|2023-01-30|0x7d6fd5f03ae86b3...|0.002455024791196405|\n",
      "|2023-01-30|0x3e4fd6fee597891...|0.002254729196924485|\n",
      "|2023-01-30|0x28e8cfface36978...|0.002254729196924485|\n",
      "|2023-01-30|0x149bb99b15e619c...|0.002254729196924485|\n",
      "+----------+--------------------+--------------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "# calculate transaction fee in eth\n",
    "df = raw.withColumn('txn_fee', raw.receipt_gas_used * raw.receipt_effective_gas_price / 1.0e18)\n",
    "df.select(['date', 'hash','txn_fee']).show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
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     "text": [
      "+----------+-------------------+------------+--------------------+-------------------+\n",
      "|      date|    block_timestamp|block_number|          block_hash|            txn_fee|\n",
      "+----------+-------------------+------------+--------------------+-------------------+\n",
      "|2021-09-01|2021-09-01 00:00:12|    13136427|0x1c3d38ad2873c0f...|  4.500235296531713|\n",
      "|2021-09-01|2021-09-01 00:00:40|    13136428|0x6426cbb21bddc09...| 1.1043923577333805|\n",
      "|2021-09-01|2021-09-01 00:00:44|    13136429|0x1025103470c75a6...|0.15909387107386222|\n",
      "|2021-09-01|2021-09-01 00:00:46|    13136430|0x4cbb6f647eb936c...|  0.977156225798188|\n",
      "|2021-09-01|2021-09-01 00:00:48|    13136431|0x40aa76a3056da15...|  4.240270822995878|\n",
      "|2021-09-01|2021-09-01 00:01:24|    13136432|0x40e74037f8fa61a...|  3.940043315604158|\n",
      "|2021-09-01|2021-09-01 00:01:39|    13136433|0x6813fa509cfc0d1...| 2.1775399984839092|\n",
      "|2021-09-01|2021-09-01 00:01:40|    13136434|0x90b33d383fb1a25...| 0.8333826916638579|\n",
      "|2021-09-01|2021-09-01 00:01:45|    13136435|0x669a39f6f7ca401...|  4.451281750032257|\n",
      "|2021-09-01|2021-09-01 00:02:28|    13136436|0xa9ab18a8f9e1f79...|  4.837225403253913|\n",
      "+----------+-------------------+------------+--------------------+-------------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "# compute total txn fee for each block\n",
    "df = df.groupBy(['date','block_timestamp','block_number','block_hash']) \\\n",
    "            .sum('txn_fee') \\\n",
    "            .withColumnRenamed('sum(txn_fee)', 'txn_fee') \\\n",
    "            .sort('block_number')\n",
    "df.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Further aggregate into daily frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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     "output_type": "stream",
     "text": [
      "+----------+------------------+\n",
      "|      date|           txn_fee|\n",
      "+----------+------------------+\n",
      "|2021-09-01|15120.502550905643|\n",
      "|2021-09-02|11645.607308309774|\n",
      "|2021-09-03|16008.193498750275|\n",
      "|2021-09-04|12468.815312722956|\n",
      "|2021-09-05|11617.029369845292|\n",
      "|2021-09-06|12226.618501032051|\n",
      "|2021-09-07| 21057.83448331158|\n",
      "|2021-09-08|16529.722432752307|\n",
      "|2021-09-09| 16807.09511707568|\n",
      "|2021-09-10|10913.772164776334|\n",
      "+----------+------------------+\n",
      "only showing top 10 rows"
     ]
    }
   ],
   "source": [
    "df_daily = df.groupBy('date') \\\n",
    "            .sum('txn_fee') \\\n",
    "            .withColumnRenamed('sum(txn_fee)', 'txn_fee') \\\n",
    "            .sort('date')\n",
    "df_daily.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "df_pd = df.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3626676"
     ]
    }
   ],
   "source": [
    "len(df_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(df_pd['block_number'], df_pd['txn_fee'])\n",
    "plt.title(\"Ethereum transaction fee by block\")\n",
    "plt.xlabel(\"Block number\")\n",
    "plt.ylabel(\"Transaction fee (ETH)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_daily_pd = df_daily.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "560"
     ]
    }
   ],
   "source": [
    "len(df_daily_pd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(df_daily_pd['date'], df_daily_pd['txn_fee'])\n",
    "plt.title(\"Ethereum transaction fee by date\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Transaction fee (ETH)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By plotting the graphs, we see that while transaction fees on the block level has been noisy, especially before the merge, fees on the daily graph has been fairly stable.  \n",
    "But what happened in May 2022? By doing some web search, I found that the spike in transaction fees was due to a newly launched NFT by Yoga Labs causing gas prices to spike to all time highs on May 1st, 2022. https://beincrypto.com/ethereum-gas-fees-spike-highs-latest-nft-hype/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average gas price over time\n",
    "prices = raw.groupBy(['date']) \\\n",
    "            .avg('receipt_effective_gas_price') \\\n",
    "            .withColumnRenamed('avg(receipt_effective_gas_price)', 'avg_gas_price') \\\n",
    "            .sort('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prices_pd = prices.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(prices_pd['date'], prices_pd['avg_gas_price'])\n",
    "plt.title(\"Ethereum average gas price by date\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Gas price (wei)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The gas prices chart moves more than the transaction fee chart. This can be explained by elastic demand for transactions in response to gas prices - when prices are higher less transactions are made. Therefore the series for transaction fee is smoother."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing for time series\n",
    "I conduct a stationarity test and a serial correlation test on the txn_fee column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# JupyterHub on EMR does not allow direct import so I copied the following code from time_series_tests.py. \n",
    "# Code is modified from my own code written for an econometrics class.\n",
    "\n",
    "\"\"\"\n",
    "This file implements three functions used for time series tests:\n",
    "Correlogram, Ljung_Box_Q_test, and Check_Stationarity\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def autocov(z, j):\n",
    "    \"\"\"\n",
    "    autocovariance function of j-th order\n",
    "    z: data series (array)\n",
    "    j: order\n",
    "    \"\"\"\n",
    "    n = len(z)\n",
    "    z_mean = np.mean(z)\n",
    "\n",
    "    gamma = 0.0\n",
    "    for t in range(j,n):\n",
    "        gamma += (z[t] - z_mean) * (z[t-j] - z_mean)\n",
    "    gamma = gamma/n\n",
    "\n",
    "    return gamma\n",
    "\n",
    "\n",
    "def autocorr(z, j):\n",
    "    \"\"\"\"\n",
    "    autocorrelation function of j-th order\n",
    "    z: data series (array)\n",
    "    j: order\n",
    "    \"\"\"\n",
    "    gamma_0 = autocov(z,0)\n",
    "    gamma_j = autocov(z,j)\n",
    "    rho = gamma_j / gamma_0\n",
    "\n",
    "    return rho\n",
    "\n",
    "\n",
    "# Correlogram\n",
    "def Correlogram(z, max_lag=40, plot=False):\n",
    "    # z: data series\n",
    "    # max_lag: number of lags\n",
    "    correlogram = []\n",
    "    for j in range(max_lag+1):\n",
    "        rho_j = autocorr(z,j)\n",
    "        correlogram.append(rho_j)\n",
    "\n",
    "    # stderr\n",
    "    N = len(z)\n",
    "    stderr = 1 / np.sqrt(N)\n",
    "\n",
    "    # plot correlogram\n",
    "    if plot:\n",
    "        plt.figure(figsize=(20,8))\n",
    "        plt.plot(range(max_lag+1), correlogram, \"o-\")\n",
    "        plt.axhline(2*stderr, ls=\"--\", label='two std errs')\n",
    "        plt.axhline(-2*stderr, ls=\"--\")\n",
    "\n",
    "        plt.title(\"Correlogram\", fontsize=20)\n",
    "        plt.xlabel(\"Lag\")\n",
    "        plt.ylabel(\"sample correlation coefficient\")\n",
    "        plt.legend(fontsize=20)\n",
    "        plt.show()\n",
    "    \n",
    "    return correlogram, stderr\n",
    "\n",
    "\n",
    "def Ljung_Box_Q_test(z,j):\n",
    "    \"\"\"\n",
    "    Computes the Ljung-Box Q statistic \n",
    "        and the p-value basd on the Q test\n",
    "    See reference: Fumio Hayashi (2011) Econometrics Ch. 2.10\n",
    "    z: data series (array)\n",
    "    j: order\n",
    "    \"\"\"\n",
    "    n = len(z)\n",
    "    q = 0.0\n",
    "    for i in range(1,j+1):\n",
    "        q += autocorr(z,i)**2 / (n-i)\n",
    "    q = q * n *(n+2)\n",
    "\n",
    "    # Q-stat asympototically converges to chi-square distribution\n",
    "    p = 1 - sts.chi2.cdf(q, j)\n",
    "\n",
    "    return q, p\n",
    "\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "def Check_Stationarity(z):\n",
    "    \"\"\"\n",
    "    Performs Augmented Dickey-Fuller's test\n",
    "    z: data series (array)\n",
    "    \"\"\"\n",
    "    \n",
    "    test_result = adfuller(z)\n",
    "    print(\"ADF Statistic : %f \\n\" %test_result[0])\n",
    "    print(\"p-value : %f \\n\" %test_result[1])\n",
    "    print(\"Critical values are: \\n\")\n",
    "    print(test_result[4])\n",
    "\n",
    "    return test_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller testing results\n",
      "frequency=daily:\n",
      "ADF Statistic : -2.293400 \n",
      "\n",
      "p-value : 0.174105 \n",
      "\n",
      "Critical values are: \n",
      "\n",
      "{'1%': -3.4423174665535385, '5%': -2.866818952732754, '10%': -2.569581505602171}"
     ]
    }
   ],
   "source": [
    "# extract data series as array\n",
    "series_daily = df_daily_pd['txn_fee'].values\n",
    "\n",
    "# Stationarity test with Augmented Dickey-Fuller\n",
    "print(\"Augmented Dickey-Fuller testing results\")\n",
    "print(\"frequency=daily:\")\n",
    "ADF_results_daily = Check_Stationarity(series_daily)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ADF test statistic suggests that the data series is close to stationary, but not exactly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sample autocorrelation with Correlogram\n",
    "demeaned = series_daily - np.mean(series_daily)\n",
    "correlogram, stderr = Correlogram(demeaned, max_lag=40, plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplot plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlogram shows there is significant sample autocorrelation. Let's formalize using the Ljung-Box Q test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, bar_style='info', description='Progress:', layout=Layout(height='25px', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ljung-Box Q testing results\n",
      "frequency=daily:\n",
      "Lag: 1, Q statistic: 212.34017225797706, p value: 0.0\n",
      "Lag: 2, Q statistic: 398.40043002955946, p value: 0.0\n",
      "Lag: 3, Q statistic: 566.8822342413159, p value: 0.0\n",
      "Lag: 4, Q statistic: 731.7640630183427, p value: 0.0\n",
      "Lag: 5, Q statistic: 890.9232986158576, p value: 0.0\n",
      "Lag: 6, Q statistic: 1052.4410776598195, p value: 0.0\n",
      "Lag: 7, Q statistic: 1205.2710572348165, p value: 0.0\n",
      "Lag: 8, Q statistic: 1356.042035080581, p value: 0.0\n",
      "Lag: 9, Q statistic: 1499.6794709357262, p value: 0.0\n",
      "Lag: 10, Q statistic: 1661.1058361378193, p value: 0.0\n",
      "Lag: 11, Q statistic: 1841.8816507315903, p value: 0.0\n",
      "Lag: 12, Q statistic: 1981.1757050745086, p value: 0.0\n",
      "Lag: 13, Q statistic: 2113.9420482959167, p value: 0.0\n",
      "Lag: 14, Q statistic: 2242.4954950548236, p value: 0.0\n",
      "Lag: 15, Q statistic: 2364.652994721136, p value: 0.0\n",
      "Lag: 16, Q statistic: 2479.2796355072455, p value: 0.0\n",
      "Lag: 17, Q statistic: 2588.5654825802712, p value: 0.0\n",
      "Lag: 18, Q statistic: 2696.3889652430285, p value: 0.0\n",
      "Lag: 19, Q statistic: 2807.251228297024, p value: 0.0\n",
      "Lag: 20, Q statistic: 2920.6317981596794, p value: 0.0\n",
      "Lag: 21, Q statistic: 3035.727096691706, p value: 0.0\n",
      "Lag: 22, Q statistic: 3146.1599757494073, p value: 0.0\n",
      "Lag: 23, Q statistic: 3252.022682590873, p value: 0.0\n",
      "Lag: 24, Q statistic: 3357.650670844161, p value: 0.0\n",
      "Lag: 25, Q statistic: 3462.8641001975366, p value: 0.0\n",
      "Lag: 26, Q statistic: 3583.348741702296, p value: 0.0\n",
      "Lag: 27, Q statistic: 3689.5007643427334, p value: 0.0\n",
      "Lag: 28, Q statistic: 3797.9309306988257, p value: 0.0\n",
      "Lag: 29, Q statistic: 3903.379345950027, p value: 0.0\n",
      "Lag: 30, Q statistic: 4003.365183837403, p value: 0.0\n",
      "Lag: 31, Q statistic: 4098.001654967203, p value: 0.0\n",
      "Lag: 32, Q statistic: 4192.704861178636, p value: 0.0\n",
      "Lag: 33, Q statistic: 4285.881878413066, p value: 0.0\n",
      "Lag: 34, Q statistic: 4394.093503760182, p value: 0.0\n",
      "Lag: 35, Q statistic: 4497.4801691130615, p value: 0.0\n",
      "Lag: 36, Q statistic: 4590.311020718744, p value: 0.0\n",
      "Lag: 37, Q statistic: 4681.497534468056, p value: 0.0\n",
      "Lag: 38, Q statistic: 4768.7382178539165, p value: 0.0\n",
      "Lag: 39, Q statistic: 4848.643824776342, p value: 0.0"
     ]
    }
   ],
   "source": [
    "# Ljung-Box Q test\n",
    "\n",
    "# daily frequency\n",
    "max_order_daily = 40\n",
    "Qtest_pval_daily = []\n",
    "print(\"Ljung-Box Q testing results\")\n",
    "print(\"frequency=daily:\")\n",
    "for order in range(1, max_order_daily):\n",
    "    q, p = Ljung_Box_Q_test(series_daily, order)\n",
    "    print(f\"Lag: {order}, Q statistic: {q}, p value: {p}\")\n",
    "    Qtest_pval_daily.append(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Ljung-Box Q test suggests significant serial correlation, indicating the neccessity to use autoregression methods in the modeling process."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "PySpark",
   "language": "",
   "name": "pysparkkernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "python",
    "version": 3
   },
   "mimetype": "text/x-python",
   "name": "pyspark",
   "pygments_lexer": "python3"
  }
 },
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}