add tsv and read/write exercises

Author: fabridamicelli

book/062_read_and_write.ipynb

@@ -432,6 +432,19 @@
     "Material under construction\n",
     "🚧"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4923c854-bb58-4ab0-8ec5-f9a7815bb7ab",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Now that we now how to read files from within python, we can tidy up things a bit from previous chapter.\n",
+    "\n",
+    "1) Download the data from the repository [here](https://github.com/OpenNeuroDatasets/ds005588/blob/main/sub-336/func/sub-336_task-respo_run-2_events.tsv) into a local file on your computer.\n",
+    "2) Write a function that takes a path and returns the list of dictionaries from exercise 8.5. Read the file content using `pathlib`."
+   ]
   }
  ],
  "metadata": {
@@ -450,7 +463,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,

book/063_common_data_formats.ipynb

@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "0dad7357-4e14-4d1e-9856-0f6b487aff7b",
    "metadata": {},
    "outputs": [
@@ -160,7 +160,7 @@
        "3  2020-01-03  x19   28"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "16cd3840-8bcf-411d-97d4-073bd7451543",
    "metadata": {},
    "outputs": [],
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 6,
    "id": "4831254b-2b1a-4b52-a105-214005c79048",
    "metadata": {},
    "outputs": [
@@ -212,6 +212,137 @@
     "!cat /tmp/data.csv"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "be179048-6908-470d-aab2-e2b3690aea81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| code-fold: true\n",
+    "#| output: false\n",
+    "\n",
+    "df.to_csv(\"/tmp/data.tsv\", sep=\"\\t\", index=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d498565-0f46-40dc-8ad8-56cfcc9f2db6",
+   "metadata": {},
+   "source": [
+    "## TSV\n",
+    "Similar to CSV, [Tab Separated Values format](https://en.wikipedia.org/wiki/Tab-separated_values) uses Tabs instead of comma to separate values of each line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b530c8be-648d-46dd-913e-1d0bc0534e38",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "date\tid\tage\n",
+      "2020-01-01\tx12\t19\n",
+      "2020-01-02\tx11\t23\n",
+      "2020-01-02\tx3\t22\n",
+      "2020-01-03\tx19\t28\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat /tmp/data.tsv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98550025-bf16-4876-81dd-6acd2f058ec5",
+   "metadata": {},
+   "source": [
+    "Luckily `pandas` can handle that too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a118b390-3e9c-4bd5-a925-eb68bd089f2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>date</th>\n",
+       "      <th>id</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-01-01</td>\n",
+       "      <td>x12</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>x11</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>x3</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-01-03</td>\n",
+       "      <td>x19</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         date   id  age\n",
+       "0  2020-01-01  x12   19\n",
+       "1  2020-01-02  x11   23\n",
+       "2  2020-01-02   x3   22\n",
+       "3  2020-01-03  x19   28"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/tmp/data.tsv\", sep=\"\\t\")\n",
+    "df"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9ffc3c66-3e20-4d95-93bd-f20312f649a0",
@@ -851,12 +982,17 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17fd5529-094d-463a-98cf-2ca6e7a47746",
+   "cell_type": "markdown",
+   "id": "b6c484e3-d432-4c77-8df1-c12ac77b3146",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Exercises\n",
+    "Download from [this public repo](https://github.com/OpenNeuroDatasets/ds005588) the files \n",
+    "`dataset_description.json`, `participants.tsv `.\n",
+    "\n",
+    "1) Read in the file `dataset_description.json` and print the field `Description`.\n",
+    "2) Read in the file `participants.tsv ` and print the mean of all the values of the column `age`."
+   ]
   }
  ],
  "metadata": {
@@ -875,7 +1011,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,

book/06_imports.ipynb

@@ -196,6 +196,9 @@
     "```python\n",
     "# preprocessing.py\n",
     "\n",
+    "\n",
+    "# TODO: add missing imports to make it work\n",
+    "\n",
     "def cleanup(data):\n",
     "    ... \n",
     "    \n",

book/_quarto.yml

@@ -42,8 +42,8 @@ book:
     - 05_comprehensions.ipynb
     - lab_builtin_for_the_win.ipynb
     - 06_imports.ipynb
-    - 061_dependencies.ipynb
     - 062_read_and_write.ipynb
+    - 061_dependencies.ipynb
     - 063_common_data_formats.ipynb
     - 051_oop.ipynb
     - 08_numpy.ipynb

pyproject.toml

@@ -8,4 +8,5 @@ dependencies = [
     "jupyterlab>=4.4.10",
     "jupyterlab-spellchecker>=0.8.4",
     "jupyterlab-vim>=4.1.4",
+    "pandas>=2.3.3",
 ]