Update all notebook to pandas version 0.18.

Author: jonathanrocher

climate_timeseries/climate_timeseries.ipynb

@@ -91,7 +91,7 @@
    "outputs": [],
    "source": [
     "# Change this cell to the demo location on YOUR machine\n",
-    "%cd ~/Projects/SciPy2015_pandas_tutorial/demos/climate_timeseries/\n",
+    "%cd ~/Projects/pandas_tutorial/climate_timeseries/\n",
     "%ls"
    ]
   },
@@ -1714,7 +1714,7 @@
    "source": [
     "# Frequencies can be specified as strings: \"us\", \"ms\", \"S\", \"T\", \"H\", \"D\", \"B\", \"W\", \"M\", \"A\", \"3min\", \"2h20\", ...\n",
     "# More aliases at http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases\n",
-    "full_globe_temp.resample(\"M\")"
+    "full_globe_temp.resample(\"M\").mean()"
    ]
   },
   {
@@ -1725,7 +1725,7 @@
    },
    "outputs": [],
    "source": [
-    "full_globe_temp.resample(\"10A\", how=\"mean\")"
+    "full_globe_temp.resample(\"10A\").mean()"
    ]
   },
   {
@@ -1920,7 +1920,7 @@
    },
    "outputs": [],
    "source": [
-    "local_sea_level_stations.sort(\"Date\")"
+    "local_sea_level_stations.sort_values(by=\"Date\")"
    ]
   },
   {
@@ -1938,7 +1938,7 @@
    },
    "outputs": [],
    "source": [
-    "local_sea_level_stations.sort([\"Date\", \"Country\"], ascending=False)"
+    "local_sea_level_stations.sort_values(by=[\"Date\", \"Country\"], ascending=False)"
    ]
   },
   {
@@ -2186,7 +2186,9 @@
    "outputs": [],
    "source": [
     "full_globe_temp.plot()\n",
-    "pd.rolling_mean(full_globe_temp, 10).plot(figsize=LARGE_FIGSIZE)"
+    "rolled_series = full_globe_temp.rolling(window=10, center=False)\n",
+    "print rolled_series\n",
+    "rolled_series.mean().plot(figsize=LARGE_FIGSIZE)"
    ]
   },
   {
@@ -2648,7 +2650,7 @@
    },
    "outputs": [],
    "source": [
-    "european_stations.sort(\"Country\")"
+    "european_stations.sort_values(by=\"Country\")"
    ]
   },
   {
@@ -2817,7 +2819,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "There are 2 objects constructors inside Pandas and inside `statsmodels`. There has been talks about merging the 2 into SM, but that hasn't happened yet. OLS in statsmodels allows more complex formulas:"
+    "The recommeded way to build ordinaty least square regressions is by using `statsmodels`."
    ]
   },
   {
@@ -2888,39 +2890,6 @@
     "plt.legend(loc=\"upper left\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OLS in pandas requires to pass a `y` series and an `x` series to do a fit of the form `y ~ x`. But the formula can be more complex by providing a `DataFrame` for x and reproduce a formula of the form `y ~ x1 + x2`. \n",
-    "\n",
-    "Also, OLS in pandas allows to do rolling and expanding OLS:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from pandas.stats.api import ols as pdols"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Same fit as above:\n",
-    "pd_model = pdols(y=mean_sea_level[\"mean_global\"], x=mean_sea_level[[\"northern_hem\", \"southern_hem\"]])\n",
-    "pd_model"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -3140,7 +3109,7 @@
    "source": [
     "# Not constant reads apparently. Let's downscale the frequency of the sea levels \n",
     "# to monthly, like the temperature reads we have:\n",
-    "monthly_mean_sea_level = mean_sea_level.resample(\"MS\").to_period()\n",
+    "monthly_mean_sea_level = mean_sea_level.resample(\"MS\").mean().to_period()\n",
     "monthly_mean_sea_level"
    ]
   },
@@ -3259,8 +3228,9 @@
    },
    "outputs": [],
    "source": [
-    "model = sm.ols(\"southern_hem ~ global_temp\", data=aligned_monthly_data).fit()\n",
-    "model.rsquared"
+    "model = sm.ols(\"southern_hem ~ global_temp\", data=aligned_monthly_data)\n",
+    "params = model.fit()\n",
+    "params.rsquared"
    ]
   },
   {
@@ -3278,7 +3248,7 @@
    },
    "outputs": [],
    "source": [
-    "aligned_yearly_data = aligned_monthly_data.resample(\"A\")\n",
+    "aligned_yearly_data = aligned_monthly_data.resample(\"A\").mean()\n",
     "aligned_yearly_data.plot()"
    ]
   },
@@ -3329,7 +3299,7 @@
    "source": [
     "import statsmodels as sm\n",
     "# Let's remove seasonal variations by resampling annually\n",
-    "data = giss_temp_series.resample(\"A\").to_timestamp()\n",
+    "data = giss_temp_series.resample(\"A\").mean().to_timestamp()\n",
     "ar_model = sm.tsa.ar_model.AR(data, freq='A')\n",
     "ar_res = ar_model.fit(maxlag=60, disp=True)"
    ]
@@ -3370,36 +3340,6 @@
    "source": [
     "# Your code here"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Want to practice more?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**EXERCISE (computations):** Refer to `exercises/stock_returns/stock_returns.py`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**EXERCISE (stats, groupby, timeseries):** Refer to `exercises/pandas_wind_statistics/pandas_wind_statistics.py`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {