RowGroup-Example.ipynb

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   "cell_type": "code",
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   "source": [
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "df = pd.DataFrame({'a': np.arange(2**20)})"
   ]
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "df.to_parquet('test.parquet')"
   ]
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   "source": [
    "df.to_parquet('test2.parquet', chunk_size=2**19)"
   ]
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   "source": [
    "pq.ParquetFile('test.parquet').num_row_groups"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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    "pq.ParquetFile('test2.parquet').num_row_groups"
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   "cell_type": "code",
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     "text": [
      "25.7 ms ± 401 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.read_parquet('test.parquet')"
   ]
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   "cell_type": "code",
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   "metadata": {},
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     "text": [
      "25.5 ms ± 266 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.read_parquet('test2.parquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use case: Read all rows with a > 600000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 ms ± 437 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit pd.read_parquet('test.parquet').query('a > 600000')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "split_file = pq.ParquetFile('test2.parquet')\n",
    "rg = split_file.metadata.row_group(0)\n",
    "rg.column(0).statistics.max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.1 ms ± 75 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Values with a > 600000' are only in the second RowGroup, so only read that\n",
    "%timeit split_file.read_row_group(1).to_pandas().query('a > 600000')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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