diff --git a/exploration/communication.ipynb b/exploration/communication.ipynb
index 5fc0072..13ff65d 100644
--- a/exploration/communication.ipynb
+++ b/exploration/communication.ipynb
@@ -221,6 +221,277 @@
"df_sms = get_sms_data([\"nokia_0000003\"])\n",
"count_comms(df_sms)"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Explore the whole dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Call data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import participants.query_db"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "participants_inactive_usernames = participants.query_db.get_usernames()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_calls_inactive = get_call_data(participants_inactive_usernames)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_calls_features = count_comms(df_calls_inactive)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " call_type | \n",
+ " no_incoming | \n",
+ " no_outgoing | \n",
+ " no_missed | \n",
+ " duration_incoming | \n",
+ " duration_outgoing | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count | \n",
+ " 43.000000 | \n",
+ " 44.000000 | \n",
+ " 38.000000 | \n",
+ " 43.000000 | \n",
+ " 44.000000 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 27.604651 | \n",
+ " 37.727273 | \n",
+ " 9.105263 | \n",
+ " 5926.813953 | \n",
+ " 7220.409091 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 37.445923 | \n",
+ " 50.961620 | \n",
+ " 13.337185 | \n",
+ " 7140.290568 | \n",
+ " 11331.095182 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 89.000000 | \n",
+ " 2.000000 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 6.500000 | \n",
+ " 6.750000 | \n",
+ " 2.000000 | \n",
+ " 924.500000 | \n",
+ " 823.500000 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 15.000000 | \n",
+ " 21.000000 | \n",
+ " 5.000000 | \n",
+ " 3258.000000 | \n",
+ " 2491.000000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 33.000000 | \n",
+ " 37.500000 | \n",
+ " 9.000000 | \n",
+ " 8762.500000 | \n",
+ " 8089.500000 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 196.000000 | \n",
+ " 258.000000 | \n",
+ " 66.000000 | \n",
+ " 31146.000000 | \n",
+ " 55270.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "call_type no_incoming no_outgoing no_missed duration_incoming \\\n",
+ "count 43.000000 44.000000 38.000000 43.000000 \n",
+ "mean 27.604651 37.727273 9.105263 5926.813953 \n",
+ "std 37.445923 50.961620 13.337185 7140.290568 \n",
+ "min 1.000000 1.000000 1.000000 89.000000 \n",
+ "25% 6.500000 6.750000 2.000000 924.500000 \n",
+ "50% 15.000000 21.000000 5.000000 3258.000000 \n",
+ "75% 33.000000 37.500000 9.000000 8762.500000 \n",
+ "max 196.000000 258.000000 66.000000 31146.000000 \n",
+ "\n",
+ "call_type duration_outgoing \n",
+ "count 44.000000 \n",
+ "mean 7220.409091 \n",
+ "std 11331.095182 \n",
+ "min 2.000000 \n",
+ "25% 823.500000 \n",
+ "50% 2491.000000 \n",
+ "75% 8089.500000 \n",
+ "max 55270.000000 "
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_calls_features.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "calls_number = pd.wide_to_long(\n",
+ " df_calls_features.reset_index(), \n",
+ " i=\"participant_id\", \n",
+ " j=\"call_type\", \n",
+ " stubnames=\"no\", \n",
+ " sep=\"_\", \n",
+ " suffix=\"\\D+\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
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+ "