{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Python Pandas String To Integer And Integer To String Of DataFrame</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python Pandas is a great library for doing data analysis. While doing the analysis, we have to often convert data from one format to another. In this tutorial I will show you how to convert String to Integer format and vice versa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two primary ways to convert data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ol><li><b>astype()</li></b><li><b>to_numeric()</b></li></ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we dive in to each of these methods. Lets first talk about our data for this exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets create a dummy dataframe with 5 student with their names and ids. For real example checkout <a href=\"https://www.nbshare.io/notebook/716961781/Merge-and-Join-DataFrames-with-Pandas-in-Python/\">Merge and Join DataFrames with Pandas in Python</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "studentinfo = {'studentname': ['John','Kyle','Chloe'],\n",
    "        'studentid': [1,2,3]\n",
    "        }\n",
    "\n",
    "df = DataFrame(studentinfo, columns= ['studentname', 'studentid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok our dataframe is created. Lets check the datatypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "studentname    object\n",
       "studentid       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok our studentname column is type 'object' and studentid is int64."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Convert Integer To Str Using astype() method of Python Pandas Dataframe </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets convert our column studentid column from int to str first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['studentid'].astype('str').dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see above astype('str') has converted integer column to string data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We haven't save our new data yet. Lets save our new data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['studentid'] = df['studentid'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['studentid'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Convert Str To Int Using astype() method of Python Pandas Dataframe </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly we can convert back String to Integer back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['studentid'] = df['studentid'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['studentid'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Convert Str To Int Using to_numeric() method of Python Pandas Dataframe </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first convert to string using our astype method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['studentid'] = df['studentid'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['studentid'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok lets convert our object type to int now using to_numeric() method of Dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: studentid, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df['studentid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There you go, we got the int64 data type back."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to_numeric has few options which are worth mentioning here. We can use the argument downcast to specify data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "to_numeric has following data types int8(signed), int64(default), float32(float) and float64(default). It has data types for date too but I will let you explore that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: studentid, dtype: int8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df['studentid'],downcast='signed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got int8 with signed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "Name: studentid, dtype: float32"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df['studentid'],downcast='float')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your data might have values which couldn't be converted to a particular data type and raise an error. Lets do an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>How To Handle Empty Values While Converting Data From Str To Int DataFrame</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets add an empty value to our dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "studentinfo = {'studentname': ['John','Kyle','Chloe','Renee'],\n",
    "        'studentid': [1,2,3,\"\"]\n",
    "        }\n",
    "df = DataFrame(studentinfo, columns= ['studentname', 'studentid'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our dataframe we added a new student name Renee with student id entry empty. Lets first check our data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "studentname    object\n",
       "studentid      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok one point to notice here is that, since studentid has an empty entry. DataFrame method converted the id column to object by default."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try our astype(int) method now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['studentid'].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I got the following error..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ValueError: invalid literal for int() with base 10: ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "astype has option 'errors' which is by default set to errors='raise'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can set it to errors='ignore' to get rid of above error. Lets try that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3     \n",
       "Name: studentid, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['studentid'].astype('int',errors='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We got rid of the above error but still the data type didnt change from object to int, but it is still object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Therefore better way is to use to_numeric() method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframe to_numeric() method has option errors='coerce', which will convert non numeric values to NaN and at the same time convert the data type to int. Lets try that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    NaN\n",
       "Name: studentid, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df['studentid'],errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see above the non numeric value got changed to NaN, but by default we got the data type float64 although numeric but not int. Lets try to specify the downcast=signed to get int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    NaN\n",
       "Name: studentid, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(df['studentid'],errors='coerce',downcast='signed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No we didnt' get the int8 value even with downcast='signed' but instead got float64."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Wrap Up!</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This post has touched upon basics of astype() and to_numeric() method. There are other data types which can be casted to using the above two methods. Please checkout yourself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Related Topics</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.nbshare.io/notebook/843265033/How-to-Export-Pandas-DataFrame-to-a-CSV-File/\">export pandas dataframe to csv</a>\n",
    "\n",
    "<a href=\"https://www.nbshare.io/notebook/204214467/How-to-Plot-a-Histogram-in-Python/\">how to plot histogram in python</a>\n",
    "\n",
    "<a href=\"https://www.nbshare.io/notebook/886682215/How-to-Convert-Python-Pandas-DataFrame-into-a-List/\">create pandas dataframe from list</a>"
   ]
  }
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