PreFall2018/Deep-and-Shallow-Copies.ipynb

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   "source": [
    "# Deep and Shallow Copies\n",
    "This short lecture addresses a common source of bugs in python (and other languages). This is illustrated below."
   ]
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     "text": [
      "[1, 2, 3, 4] [1, 2, 3, 4]\n"
     ]
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   "source": [
    "# BUGGY Code\n",
    "# Create a list\n",
    "list1 = [1, 2, 3]\n",
    "# Make a copy\n",
    "list2 = list1\n",
    "# Update the copy\n",
    "list2.append(4)\n",
    "# BUG: list1 has changed too!\n",
    "print(list1, list2)\n"
   ]
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    "There are different ways in which an object can be copied. Typically, these are characterized as:\n",
    "- *Deep copy*: Allocate new memory and copy the object to this memory. Modifications to this memory has no effect on the original object.\n",
    "- *Shallow copy*: Create a pointer to the object. Modifications to the object are seen by all code with a pointer to the object."
   ]
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    "In general, assigning one python variable to another creates a shallow copy."
   ]
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     "text": [
      "140209619885000 140209619885000\n"
     ]
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   "source": [
    "# Create a list\n",
    "list1 = [1, 2, 3]\n",
    "# Make a copy\n",
    "list2 = list1\n",
    "# Use \"id\" to see if list1 and list2 point to the same object\n",
    "print(id(list1), id(list2))"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "To get a deep copy, you  must use a special method. For lists use the `list` function. For `pandas.DataFrame`, use the `copy` method."
   ]
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      "140209619467464 140209619467528\n",
      "[1, 2, 3] [1, 2, 3, -1]\n"
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    "# Making a deep copy of a list.\n",
    "list1 = [1, 2, 3]\n",
    "list2 = list(list1)  # Make a copy of var3 to manipulate it\n",
    "print(id(list1), id(list2))\n",
    "list2.append(-1)\n",
    "# Changing var4 does not change var3\n",
    "print(list1, list2)"
   ]
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   "source": [
    "# Deep and shallow copies for DataFrames\n",
    "import pandas as pd\n",
    "df1 = pd.DataFrame({'a':range(10), 'b': range(10)})\n",
    "df1\n",
    "df2 = df1\n",
    "del df2['a']\n",
    "df1"
   ]
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   "cell_type": "code",
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     "text": [
      "140209091735056 140209091735056\n"
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   "source": [
    "print(id(df1), id(df2))"
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  }
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