diff --git a/exploration/communication.ipynb b/exploration/communication.ipynb index 13ff65d..7c4c6d9 100644 --- a/exploration/communication.ipynb +++ b/exploration/communication.ipynb @@ -3,10 +3,13 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "import seaborn as sns" + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" ] }, { @@ -251,30 +254,127 @@ "metadata": {}, "outputs": [], "source": [ - "participants_inactive_usernames = participants.query_db.get_usernames()" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ + "participants_inactive_usernames = participants.query_db.get_usernames()\n", "df_calls_inactive = get_call_data(participants_inactive_usernames)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "call_type no_incoming no_outgoing no_missed duration_incoming \\\n", + "participant_id \n", + "13 3.0 21.0 2.0 342.0 \n", + "14 16.0 22.0 11.0 1873.0 \n", + "15 3.0 2.0 NaN 310.0 \n", + "16 4.0 6.0 3.0 1963.0 \n", + "17 20.0 60.0 8.0 5789.0 \n", + "\n", + "call_type duration_outgoing \n", + "participant_id \n", + "13 2836.0 \n", + "14 2789.0 \n", + "15 19.0 \n", + "16 849.0 \n", + "17 17046.0 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df_calls_features = count_comms(df_calls_inactive)" + "df_calls_features = count_comms(df_calls_inactive)\n", + "df_calls_features.head()" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -308,27 +408,27 @@ " \n", " \n", " count\n", - " 43.000000\n", - " 44.000000\n", - " 38.000000\n", - " 43.000000\n", - " 44.000000\n", + " 47.000000\n", + " 48.000000\n", + " 42.000000\n", + " 47.000000\n", + " 48.000000\n", " \n", " \n", " mean\n", - " 27.604651\n", - " 37.727273\n", - " 9.105263\n", - " 5926.813953\n", - " 7220.409091\n", + " 29.659574\n", + " 41.270833\n", + " 10.809524\n", + " 7222.297872\n", + " 8462.750000\n", " \n", " \n", " std\n", - " 37.445923\n", - " 50.961620\n", - " 13.337185\n", - " 7140.290568\n", - " 11331.095182\n", + " 37.325988\n", + " 50.983827\n", + " 14.385355\n", + " 8790.037189\n", + " 11965.518908\n", " \n", " \n", " min\n", @@ -340,34 +440,34 @@ " \n", " \n", " 25%\n", - " 6.500000\n", - " 6.750000\n", - " 2.000000\n", - " 924.500000\n", - " 823.500000\n", + " 7.500000\n", + " 7.750000\n", + " 2.250000\n", + " 1174.000000\n", + " 891.750000\n", " \n", " \n", " 50%\n", - " 15.000000\n", - " 21.000000\n", - " 5.000000\n", - " 3258.000000\n", - " 2491.000000\n", + " 16.000000\n", + " 22.500000\n", + " 6.500000\n", + " 3471.000000\n", + " 2812.500000\n", " \n", " \n", " 75%\n", - " 33.000000\n", - " 37.500000\n", - " 9.000000\n", - " 8762.500000\n", - " 8089.500000\n", + " 37.000000\n", + " 61.250000\n", + " 10.750000\n", + " 10441.000000\n", + " 12758.500000\n", " \n", " \n", " max\n", " 196.000000\n", " 258.000000\n", " 66.000000\n", - " 31146.000000\n", + " 40232.000000\n", " 55270.000000\n", " \n", " \n", @@ -376,27 +476,27 @@ ], "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", + "count 47.000000 48.000000 42.000000 47.000000 \n", + "mean 29.659574 41.270833 10.809524 7222.297872 \n", + "std 37.325988 50.983827 14.385355 8790.037189 \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", + "25% 7.500000 7.750000 2.250000 1174.000000 \n", + "50% 16.000000 22.500000 6.500000 3471.000000 \n", + "75% 37.000000 61.250000 10.750000 10441.000000 \n", + "max 196.000000 258.000000 66.000000 40232.000000 \n", "\n", "call_type duration_outgoing \n", - "count 44.000000 \n", - "mean 7220.409091 \n", - "std 11331.095182 \n", + "count 48.000000 \n", + "mean 8462.750000 \n", + "std 11965.518908 \n", "min 2.000000 \n", - "25% 823.500000 \n", - "50% 2491.000000 \n", - "75% 8089.500000 \n", + "25% 891.750000 \n", + "50% 2812.500000 \n", + "75% 12758.500000 \n", "max 55270.000000 " ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -407,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -423,22 +523,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -455,22 +555,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -492,6 +592,201 @@ ")\n", "sns.displot(calls_duration, x=\"duration\", hue=\"call_type\", multiple=\"dodge\", height=8, log_scale=(True, False))" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Most frequent contacts by participant" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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id_idtimestampdevice_idcall_typecall_durationtraceparticipant_idusernamefreqcontact_id
382450482411618926744570bd4b9ded-fce7-442c-8443-9fbd54d843e52218ed9d4bc2d3436dedfecce58ddefbe0a14ce49ee259uploader_21880322
382550432361618912135563bd4b9ded-fce7-442c-8443-9fbd54d843e52194705a0d9f221925228b13cbb8949e7cc5727380c059uploader_21880146
382650502431618940512431bd4b9ded-fce7-442c-8443-9fbd54d843e51248684d997bff096d553bdbeca6241b319df91382759uploader_21880231
382750302241618849848462bd4b9ded-fce7-442c-8443-9fbd54d843e51198684d997bff096d553bdbeca6241b319df91382759uploader_21880231
382850462391618921815857bd4b9ded-fce7-442c-8443-9fbd54d843e511230fb3ea8b63c952b9d4536f1fa236e67b8d86266959uploader_21880138
\n", + "
" + ], + "text/plain": [ + " id _id timestamp device_id \\\n", + "3824 5048 241 1618926744570 bd4b9ded-fce7-442c-8443-9fbd54d843e5 \n", + "3825 5043 236 1618912135563 bd4b9ded-fce7-442c-8443-9fbd54d843e5 \n", + "3826 5050 243 1618940512431 bd4b9ded-fce7-442c-8443-9fbd54d843e5 \n", + "3827 5030 224 1618849848462 bd4b9ded-fce7-442c-8443-9fbd54d843e5 \n", + "3828 5046 239 1618921815857 bd4b9ded-fce7-442c-8443-9fbd54d843e5 \n", + "\n", + " call_type call_duration trace \\\n", + "3824 2 218 ed9d4bc2d3436dedfecce58ddefbe0a14ce49ee2 \n", + "3825 2 194 705a0d9f221925228b13cbb8949e7cc5727380c0 \n", + "3826 1 24 8684d997bff096d553bdbeca6241b319df913827 \n", + "3827 1 19 8684d997bff096d553bdbeca6241b319df913827 \n", + "3828 1 123 0fb3ea8b63c952b9d4536f1fa236e67b8d862669 \n", + "\n", + " participant_id username freq contact_id \n", + "3824 59 uploader_21880 3 22 \n", + "3825 59 uploader_21880 1 46 \n", + "3826 59 uploader_21880 2 31 \n", + "3827 59 uploader_21880 2 31 \n", + "3828 59 uploader_21880 1 38 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_calls_inactive = enumerate_contacts(df_calls_inactive)\n", + "df_calls_inactive.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "df_calls_frequent = df_calls_inactive.query('contact_id < 5')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.boxplot(x=\"contact_id\", y=\"freq\", data=df_calls_frequent)" + ] } ], "metadata": { @@ -510,7 +805,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.9.2" } }, "nbformat": 4, diff --git a/features/communication.py b/features/communication.py index 13792d5..6924d42 100644 --- a/features/communication.py +++ b/features/communication.py @@ -86,8 +86,8 @@ def enumerate_contacts(comm_df: pd.DataFrame) -> pd.DataFrame: # In other words, recode the contacts into integers from 0 to n_contacts, # so that the first one is contacted the most often. contact_ids = ( - contact_counts.groupby("participant_id") # Group again for enummeration. - .cumcount() # Enummerate (count) rows *within* participants. + contact_counts.groupby("participant_id") # Group again for enumeration. + .cumcount() # Enumerate (count) rows *within* participants. .to_frame("contact_id") ) contact_counts = contact_counts.join(contact_ids)