Feat: Minor thing

templates/tpl_evaluation.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,12 +12,13 @@
     "from pathlib import Path\n",
     "from datetime import datetime\n",
     "from recopytex import flat_clear_csv, pp_q_scores\n",
+    "#import prettytable as pt\n",
     "%matplotlib inline"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "metadata": {
     "tags": [
      "parameters"
@@ -33,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -58,18 +59,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "stack_scores = pd.read_csv(csv_file)\n",
+    "stack_scores = pd.read_csv(csv_file, encoding=\"latin_1\")\n",
     "scores = flat_clear_csv(stack_scores).dropna(subset=[\"Score\"])\n",
     "scores = pp_q_scores(scores)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -145,7 +146,7 @@
        "         ALI SAID Anchourati       0.00     6.0"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -157,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -338,7 +339,7 @@
        "SAIDALI Irichad           5.00    12.0"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -350,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -367,7 +368,7 @@
        "Name: Note, dtype: float64"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -378,16 +379,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3cd95c1990>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0ae61e5cf8>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -434,7 +435,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,