Python - Pickle a Data Object [ DataFrame , List , Dict ......].ipynb

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   "source": [
    "## Pickling a Data Object in Python\n",
    "\n",
    "Priyaranjan Mohanty"
   ]
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    "## What is Pickle in python ?\n",
    "\n",
    "<b> Pickle </b> is used for serializing and de-serializing a Python object structure. \n",
    "\n",
    "Any object in python can be pickled so that it can be saved on disk. \n",
    "\n",
    "What pickle does is that it “serialises” the object first before writing it to file. \n",
    "\n",
    "Pickling is a way to convert a python object (list, dict, etc.) into a character stream. \n",
    "\n",
    "The idea is that this character stream contains all the information necessary to reconstruct the object in another python script."
   ]
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    "### Method 1 Of Pickling : Pickling a Data-Frame using Pandas"
   ]
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   "source": [
    "# import the pandas package\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read a CSV file into a Dataframe \n",
    "\n",
    "Wine_DF = pd.read_csv(\"wine_data.csv\")"
   ]
  },
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   "source": [
    "# Check the shape of DataFrame\n",
    "\n",
    "Wine_DF.shape"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the Dataframe as Pickle\n",
    "\n",
    "Wine_DF.to_pickle(\"Wine_DF.plk\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [],
   "source": [
    "# Load dataframe from pickled pandas object\n",
    "\n",
    "Wine_df_Load= pd.read_pickle(\"Wine_DF.plk\")"
   ]
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    "Wine_df_Load.shape"
   ]
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       "True"
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     "execution_count": 10,
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   "source": [
    "# Check that both the dataframes are equal or not \n",
    "\n",
    "Wine_df_Load.equals(Wine_DF)"
   ]
  },
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   "source": [
    "###  Method 2 Of Pickling : Pickling any Python Data Object ( List , Dictionary ..etc )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pickle package\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a List object\n",
    "\n",
    "List_Var = [\"Test List 1\",\"Test List 2\",\"Test List 3\",\"Test List 4\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "['Test List 1', 'Test List 2', 'Test List 3', 'Test List 4']"
      ]
     },
     "execution_count": 14,
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     "output_type": "execute_result"
    }
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   "source": [
    "List_Var"
   ]
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   "cell_type": "code",
   "execution_count": 20,
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   "source": [
    "# open the file for writing\n",
    "fileObject = open(\"Pickled_List\",'wb') \n",
    "\n",
    "# this writes the object a to the\n",
    "# file named 'Pickled_List'\n",
    "pickle.dump(List_Var,fileObject)  \n",
    "\n",
    "# here we close the fileObject\n",
    "fileObject.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the file for reading\n",
    "fileObject = open(\"Pickled_List\",'rb')  \n",
    "\n",
    " # load the object from the file into var b\n",
    "Load_List = pickle.load(fileObject) \n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "['Test List 1', 'Test List 2', 'Test List 3', 'Test List 4']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the content of the loaded list from pickle \n",
    "\n",
    "Load_List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
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     "data": {
      "text/plain": [
       "True"
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     "execution_count": 24,
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   ],
   "source": [
    "# Compare the loaded list from Pickle with original list object\n",
    "\n",
    "List_Var == Load_List"
   ]
  }
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