version 1.4

Author: Jason-M-Richards

.ipynb_checkpoints/OOP Toolkit-checkpoint.ipynb

@@ -520,6 +520,34 @@
     "    perm.fit(X_train, y_train)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Geospatial Data\n",
+    "\n",
+    "### convert start : longitude/latitude  and  end: longitude/latitude to distance\n",
+    "\n",
+    "    '''Distance equation for long,lat data used via stackoverflow from user Michael0x2a. \n",
+    "    Updated to a function that converts to mileage'''\n",
+    "    # constant values, if need to change end lat, long points, change the lat2, lon2 information\n",
+    "    lat2 = np.array(clean.Latitude)\n",
+    "    lon2 = np.array(clean.Longitude)\n",
+    "    latr = np.array(list(map(lambda x: np.radians(x), lat2)))\n",
+    "    lonr = np.array(list(map(lambda x: np.radians(x), lon2)))\n",
+    "    def distance(lat1,lon1):\n",
+    "        lat1 = np.radians(lat1)\n",
+    "        lon1 = np.radians(lon1)\n",
+    "        dlon = np.array(list(map(lambda x: (x - lon1), lonr)))\n",
+    "        dlat = np.array(list(map(lambda x: (x - lat1), latr)))\n",
+    "        a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n",
+    "        c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))\n",
+    "        # 6373.0 represents earth radius in kilometers\n",
+    "        kilo = 6373.0 * c\n",
+    "        miles = kilo * 0.62137119\n",
+    "        return miles\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -826,7 +854,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,

.ipynb_checkpoints/PCA-Preprocessing Toolkit-checkpoint.ipynb

@@ -114,7 +114,9 @@
     "- df.groupby(col1)[col2] | Returns the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics module)\n",
     "- df.pivot_table(index=col1,values=[col2,col3],aggfunc=mean) | Create a pivot table that groups by col1 and calculates the mean of col2 and col3\n",
     "- df.groupby(col1).agg(np.mean) | Find the average across all columns for every unique col1 group\n",
-    "- df.apply(np.mean) | Apply the function np.mean() across each column\n",
+    "- df.apply(function, axis=) | Apply the function across the entire DataFrame\n",
+    "    - axis must be specified 0=column, 1=row)\n",
+    "    - can be used with lambda function\n",
     "- df.applymap() | Apply function element-wise\n",
     "- df.rank() | assign ranks to entries"
    ]
@@ -153,13 +155,24 @@
     "#### Datetime\n",
     "\n",
     "- pd.to_datetime - converts a date to a datetime object\n",
-    "- pd.to_local - "
+    "- dt.tz_localize('America/New_York', ambiguous='NaT') - ambiguous argument replaces ambiguous times with NaT (not a time)\n",
+    "- dt.tz_convert('Europe/London') - converts to stated timezone\n",
+    "- dt.weekday_name - lists the day of the week for each datetime"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "#### Iterating\n",
+    "\n",
+    "- iterrows() - creates an indexed list of each row observation (like iloc, but creates index) and stores as index and Series\n",
+    "- itertuples() - like itterrows, but strores data as a special tuple, that when calling the named value can call index and all columns by . method. ex. tuple.Index, tuple.Col1, tuple.Coln\n",
+    "\n",
+    "##### including Numpy to iterate\n",
+    "- pandas is built on Numpy, so DataFrames can essentially use broadcasting methods to perform functions\n",
+    "- df['column'].values will return a Numpy array of that column's values\n"
+   ]
   },
   {
    "cell_type": "code",