7.1_data_frames.ipynb

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   "source": [
    "## Google Data Analyst - [Course 7 - Data Analysis with R Programming](https://www.coursera.org/learn/data-analysis-r/supplement/Y0Vr4/course-syllabus) [[Data Analyst]] #Google-data-analyst-course\n",
    "\n",
    "### [Week 3 - Working with data frames](https://www.coursera.org/learn/data-analysis-r/lecture/BGeQ4/working-with-data-frames)"
   ]
  },
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   "source": [
    "The course (finally) gets into using R by showing the basics of loading the diamonds dataset, like this:\n",
    "\n",
    "```\n",
    "library(ggplots2)\n",
    "data(\"diamonds\")\n",
    "View(diamonds)\n",
    "head(diamonds)\n",
    "```\n",
    "\n",
    "Which I can do the same in pandas here:\n",
    "\n",
    "### **Side note: For detailed Pandas notes, check my Colab Notebooks in Google Drive!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "url = \"https://github.com/tidyverse/ggplot2/blob/main/data-raw/diamonds.csv?raw=true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
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       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diamonds = pd.read_csv(url)\n",
    "\n",
    "df_diamonds.head()"
   ]
  },
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   "source": [
    "The `str(diamonds)` method in R gives structure info about the data frame.\n",
    "\n",
    "In pandas we use `info()` for that."
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   carat    53940 non-null  float64\n",
      " 1   cut      53940 non-null  object \n",
      " 2   color    53940 non-null  object \n",
      " 3   clarity  53940 non-null  object \n",
      " 4   depth    53940 non-null  float64\n",
      " 5   table    53940 non-null  float64\n",
      " 6   price    53940 non-null  int64  \n",
      " 7   x        53940 non-null  float64\n",
      " 8   y        53940 non-null  float64\n",
      " 9   z        53940 non-null  float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 4.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_diamonds.info()"
   ]
  },
  {
   "attachments": {},
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   "source": [
    "The `colnames()` method displays the column names. Use `columns` in pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price', 'x', 'y',\n",
       "       'z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_diamonds.columns"
   ]
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   "attachments": {},
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   "source": [
    "In R, you use the `mutate()` method in the `tidyverse` package to make changes to the data frame:\n",
    "```\n",
    "library(tidyverse) # load package\n",
    "mutate(diamonds, carat_2=carat*100) # add a \"carat_2\" column with its content being…\n",
    "```\n",
    "\n",
    "In Python, we do the same without any special method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>caratx100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "      <td>23.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
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       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
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       "      <td>21.0</td>\n",
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       "      <td>0.23</td>\n",
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       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "      <td>29.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
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       "      <td>4.34</td>\n",
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       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "      <td>72.0</td>\n",
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       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "      <td>72.0</td>\n",
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       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "      <td>86.0</td>\n",
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       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "      <td>75.0</td>\n",
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       "  </tbody>\n",
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       "<p>53940 rows × 11 columns</p>\n",
       "</div>"
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      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z  \\\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43   \n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31   \n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31   \n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63   \n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75   \n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...   \n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50   \n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61   \n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56   \n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74   \n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64   \n",
       "\n",
       "       caratx100  \n",
       "0           23.0  \n",
       "1           21.0  \n",
       "2           23.0  \n",
       "3           29.0  \n",
       "4           31.0  \n",
       "...          ...  \n",
       "53935       72.0  \n",
       "53936       72.0  \n",
       "53937       70.0  \n",
       "53938       86.0  \n",
       "53939       75.0  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diamonds['caratx100'] = df_diamonds['carat'] * 100\n",
    "df_diamonds"
   ]
  }
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