X-Git-Url: https://git.njae.me.uk/?a=blobdiff_plain;f=covid.ipynb;h=7cbbc8d2d9c427273434ac1ce0a5dac9e3c86522;hb=300ef8101cbba6f68966eacb211c3dd04f7a995f;hp=0841cdf8dbf8d44217a52880c1958b39d0b553fc;hpb=7a76e940632da2818d46e64d13d7d605f068168a;p=covid19.git
diff --git a/covid.ipynb b/covid.ipynb
index 0841cdf..7cbbc8d 100644
--- a/covid.ipynb
+++ b/covid.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 253,
+ "execution_count": 399,
"metadata": {},
"outputs": [],
"source": [
@@ -26,7 +26,7 @@
},
{
"cell_type": "code",
- "execution_count": 254,
+ "execution_count": 475,
"metadata": {},
"outputs": [],
"source": [
@@ -40,7 +40,7 @@
},
{
"cell_type": "code",
- "execution_count": 962,
+ "execution_count": 863,
"metadata": {},
"outputs": [
{
@@ -49,7 +49,7 @@
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
- "100 786k 100 786k 0 0 522k 0 0:00:01 0:00:01 --:--:-- 521k\n"
+ "100 892k 100 892k 0 0 637k 0 0:00:01 0:00:01 --:--:-- 637k\n"
]
}
],
@@ -59,7 +59,7 @@
},
{
"cell_type": "code",
- "execution_count": 963,
+ "execution_count": 864,
"metadata": {},
"outputs": [],
"source": [
@@ -69,16 +69,16 @@
},
{
"cell_type": "code",
- "execution_count": 964,
+ "execution_count": 865,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "152119"
+ "172678"
]
},
- "execution_count": 964,
+ "execution_count": 865,
"metadata": {},
"output_type": "execute_result"
}
@@ -89,7 +89,7 @@
},
{
"cell_type": "code",
- "execution_count": 901,
+ "execution_count": 866,
"metadata": {},
"outputs": [
{
@@ -129,12 +129,12 @@
"
\n",
" \n",
" 0 | \n",
- " 2020-04-29 | \n",
- " 29 | \n",
- " 4 | \n",
+ " 2020-05-08 | \n",
+ " 8 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 124 | \n",
- " 3 | \n",
+ " 171 | \n",
+ " 2 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
@@ -143,12 +143,12 @@
"
\n",
" \n",
" 1 | \n",
- " 2020-04-28 | \n",
- " 28 | \n",
- " 4 | \n",
+ " 2020-05-07 | \n",
+ " 7 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 172 | \n",
- " 0 | \n",
+ " 168 | \n",
+ " 9 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
@@ -157,12 +157,12 @@
"
\n",
" \n",
" 2 | \n",
- " 2020-04-27 | \n",
- " 27 | \n",
- " 4 | \n",
+ " 2020-05-06 | \n",
+ " 6 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 68 | \n",
- " 10 | \n",
+ " 330 | \n",
+ " 5 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
@@ -171,12 +171,12 @@
"
\n",
" \n",
" 3 | \n",
- " 2020-04-26 | \n",
- " 26 | \n",
- " 4 | \n",
+ " 2020-05-05 | \n",
+ " 5 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 112 | \n",
- " 4 | \n",
+ " 190 | \n",
+ " 5 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
@@ -185,12 +185,12 @@
"
\n",
" \n",
" 4 | \n",
- " 2020-04-25 | \n",
- " 25 | \n",
+ " 2020-05-04 | \n",
" 4 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 70 | \n",
- " 1 | \n",
+ " 235 | \n",
+ " 13 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
@@ -203,11 +203,11 @@
],
"text/plain": [
" dateRep day month year cases deaths countriesAndTerritories geoId \\\n",
- "0 2020-04-29 29 4 2020 124 3 Afghanistan AF \n",
- "1 2020-04-28 28 4 2020 172 0 Afghanistan AF \n",
- "2 2020-04-27 27 4 2020 68 10 Afghanistan AF \n",
- "3 2020-04-26 26 4 2020 112 4 Afghanistan AF \n",
- "4 2020-04-25 25 4 2020 70 1 Afghanistan AF \n",
+ "0 2020-05-08 8 5 2020 171 2 Afghanistan AF \n",
+ "1 2020-05-07 7 5 2020 168 9 Afghanistan AF \n",
+ "2 2020-05-06 6 5 2020 330 5 Afghanistan AF \n",
+ "3 2020-05-05 5 5 2020 190 5 Afghanistan AF \n",
+ "4 2020-05-04 4 5 2020 235 13 Afghanistan AF \n",
"\n",
" countryterritoryCode popData2018 continentExp \n",
"0 AFG 37172386 Asia \n",
@@ -217,7 +217,7 @@
"4 AFG 37172386 Asia "
]
},
- "execution_count": 901,
+ "execution_count": 866,
"metadata": {},
"output_type": "execute_result"
}
@@ -228,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 902,
+ "execution_count": 867,
"metadata": {},
"outputs": [
{
@@ -248,7 +248,7 @@
"dtype: object"
]
},
- "execution_count": 902,
+ "execution_count": 867,
"metadata": {},
"output_type": "execute_result"
}
@@ -259,7 +259,7 @@
},
{
"cell_type": "code",
- "execution_count": 903,
+ "execution_count": 868,
"metadata": {},
"outputs": [
{
@@ -385,23 +385,23 @@
"
\n",
" \n",
" ZW | \n",
- " 2020-04-25 | \n",
- " 25 | \n",
+ " 2020-05-04 | \n",
" 4 | \n",
+ " 5 | \n",
" 2020 | \n",
" 0 | \n",
- " 1 | \n",
+ " 0 | \n",
" Zimbabwe | \n",
" ZWE | \n",
" 14439018 | \n",
" Africa | \n",
"
\n",
" \n",
- " 2020-04-26 | \n",
- " 26 | \n",
- " 4 | \n",
+ " 2020-05-05 | \n",
+ " 5 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 2 | \n",
+ " 0 | \n",
" 0 | \n",
" Zimbabwe | \n",
" ZWE | \n",
@@ -409,9 +409,9 @@
" Africa | \n",
"
\n",
" \n",
- " 2020-04-27 | \n",
- " 27 | \n",
- " 4 | \n",
+ " 2020-05-06 | \n",
+ " 6 | \n",
+ " 5 | \n",
" 2020 | \n",
" 0 | \n",
" 0 | \n",
@@ -421,11 +421,11 @@
" Africa | \n",
"
\n",
" \n",
- " 2020-04-28 | \n",
- " 28 | \n",
- " 4 | \n",
+ " 2020-05-07 | \n",
+ " 7 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 1 | \n",
+ " 0 | \n",
" 0 | \n",
" Zimbabwe | \n",
" ZWE | \n",
@@ -433,9 +433,9 @@
" Africa | \n",
"
\n",
" \n",
- " 2020-04-29 | \n",
- " 29 | \n",
- " 4 | \n",
+ " 2020-05-08 | \n",
+ " 8 | \n",
+ " 5 | \n",
" 2020 | \n",
" 0 | \n",
" 0 | \n",
@@ -446,7 +446,7 @@
"
\n",
" \n",
"\n",
- "13829 rows à 9 columns
\n",
+ "15698 rows à 9 columns
\n",
""
],
"text/plain": [
@@ -458,11 +458,11 @@
" 2020-03-17 17 3 2020 9 0 Andorra \n",
" 2020-03-18 18 3 2020 0 0 Andorra \n",
"... ... ... ... ... ... ... \n",
- "ZW 2020-04-25 25 4 2020 0 1 Zimbabwe \n",
- " 2020-04-26 26 4 2020 2 0 Zimbabwe \n",
- " 2020-04-27 27 4 2020 0 0 Zimbabwe \n",
- " 2020-04-28 28 4 2020 1 0 Zimbabwe \n",
- " 2020-04-29 29 4 2020 0 0 Zimbabwe \n",
+ "ZW 2020-05-04 4 5 2020 0 0 Zimbabwe \n",
+ " 2020-05-05 5 5 2020 0 0 Zimbabwe \n",
+ " 2020-05-06 6 5 2020 0 0 Zimbabwe \n",
+ " 2020-05-07 7 5 2020 0 0 Zimbabwe \n",
+ " 2020-05-08 8 5 2020 0 0 Zimbabwe \n",
"\n",
" countryterritoryCode popData2018 continentExp \n",
"geoId dateRep \n",
@@ -472,16 +472,16 @@
" 2020-03-17 AND 77006 Europe \n",
" 2020-03-18 AND 77006 Europe \n",
"... ... ... ... \n",
- "ZW 2020-04-25 ZWE 14439018 Africa \n",
- " 2020-04-26 ZWE 14439018 Africa \n",
- " 2020-04-27 ZWE 14439018 Africa \n",
- " 2020-04-28 ZWE 14439018 Africa \n",
- " 2020-04-29 ZWE 14439018 Africa \n",
+ "ZW 2020-05-04 ZWE 14439018 Africa \n",
+ " 2020-05-05 ZWE 14439018 Africa \n",
+ " 2020-05-06 ZWE 14439018 Africa \n",
+ " 2020-05-07 ZWE 14439018 Africa \n",
+ " 2020-05-08 ZWE 14439018 Africa \n",
"\n",
- "[13829 rows x 9 columns]"
+ "[15698 rows x 9 columns]"
]
},
- "execution_count": 903,
+ "execution_count": 868,
"metadata": {},
"output_type": "execute_result"
}
@@ -494,7 +494,7 @@
},
{
"cell_type": "code",
- "execution_count": 904,
+ "execution_count": 869,
"metadata": {},
"outputs": [
{
@@ -615,60 +615,60 @@
" ... | \n",
" \n",
" \n",
- " 2020-04-25 | \n",
- " 25 | \n",
+ " 2020-05-04 | \n",
" 4 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 5386 | \n",
- " 768 | \n",
+ " 4339 | \n",
+ " 315 | \n",
" United_Kingdom | \n",
" GBR | \n",
" 66488991 | \n",
" Europe | \n",
"
\n",
" \n",
- " 2020-04-26 | \n",
- " 26 | \n",
- " 4 | \n",
+ " 2020-05-05 | \n",
+ " 5 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 4913 | \n",
- " 813 | \n",
+ " 3985 | \n",
+ " 288 | \n",
" United_Kingdom | \n",
" GBR | \n",
" 66488991 | \n",
" Europe | \n",
"
\n",
" \n",
- " 2020-04-27 | \n",
- " 27 | \n",
- " 4 | \n",
+ " 2020-05-06 | \n",
+ " 6 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 4463 | \n",
- " 413 | \n",
+ " 4406 | \n",
+ " 693 | \n",
" United_Kingdom | \n",
" GBR | \n",
" 66488991 | \n",
" Europe | \n",
"
\n",
" \n",
- " 2020-04-28 | \n",
- " 28 | \n",
- " 4 | \n",
+ " 2020-05-07 | \n",
+ " 7 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 4309 | \n",
- " 360 | \n",
+ " 6211 | \n",
+ " 649 | \n",
" United_Kingdom | \n",
" GBR | \n",
" 66488991 | \n",
" Europe | \n",
"
\n",
" \n",
- " 2020-04-29 | \n",
- " 29 | \n",
- " 4 | \n",
+ " 2020-05-08 | \n",
+ " 8 | \n",
+ " 5 | \n",
" 2020 | \n",
- " 3996 | \n",
- " 586 | \n",
+ " 5514 | \n",
+ " 539 | \n",
" United_Kingdom | \n",
" GBR | \n",
" 66488991 | \n",
@@ -676,7 +676,7 @@
"
\n",
" \n",
"\n",
- "121 rows à 9 columns
\n",
+ "130 rows à 9 columns
\n",
""
],
"text/plain": [
@@ -688,11 +688,11 @@
"2020-01-03 3 1 2020 0 0 United_Kingdom \n",
"2020-01-04 4 1 2020 0 0 United_Kingdom \n",
"... ... ... ... ... ... ... \n",
- "2020-04-25 25 4 2020 5386 768 United_Kingdom \n",
- "2020-04-26 26 4 2020 4913 813 United_Kingdom \n",
- "2020-04-27 27 4 2020 4463 413 United_Kingdom \n",
- "2020-04-28 28 4 2020 4309 360 United_Kingdom \n",
- "2020-04-29 29 4 2020 3996 586 United_Kingdom \n",
+ "2020-05-04 4 5 2020 4339 315 United_Kingdom \n",
+ "2020-05-05 5 5 2020 3985 288 United_Kingdom \n",
+ "2020-05-06 6 5 2020 4406 693 United_Kingdom \n",
+ "2020-05-07 7 5 2020 6211 649 United_Kingdom \n",
+ "2020-05-08 8 5 2020 5514 539 United_Kingdom \n",
"\n",
" countryterritoryCode popData2018 continentExp \n",
"dateRep \n",
@@ -702,16 +702,16 @@
"2020-01-03 GBR 66488991 Europe \n",
"2020-01-04 GBR 66488991 Europe \n",
"... ... ... ... \n",
- "2020-04-25 GBR 66488991 Europe \n",
- "2020-04-26 GBR 66488991 Europe \n",
- "2020-04-27 GBR 66488991 Europe \n",
- "2020-04-28 GBR 66488991 Europe \n",
- "2020-04-29 GBR 66488991 Europe \n",
+ "2020-05-04 GBR 66488991 Europe \n",
+ "2020-05-05 GBR 66488991 Europe \n",
+ "2020-05-06 GBR 66488991 Europe \n",
+ "2020-05-07 GBR 66488991 Europe \n",
+ "2020-05-08 GBR 66488991 Europe \n",
"\n",
- "[121 rows x 9 columns]"
+ "[130 rows x 9 columns]"
]
},
- "execution_count": 904,
+ "execution_count": 869,
"metadata": {},
"output_type": "execute_result"
}
@@ -722,7 +722,36 @@
},
{
"cell_type": "code",
- "execution_count": 905,
+ "execution_count": 870,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "day 17\n",
+ "month 4\n",
+ "year 2020\n",
+ "cases 4617\n",
+ "deaths 1029\n",
+ "countriesAndTerritories United_Kingdom\n",
+ "countryterritoryCode GBR\n",
+ "popData2018 66488991\n",
+ "continentExp Europe\n",
+ "Name: (UK, 2020-04-17 00:00:00), dtype: object"
+ ]
+ },
+ "execution_count": 870,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "base_data.loc['UK', '2020-04-17']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 871,
"metadata": {},
"outputs": [
{
@@ -795,7 +824,7 @@
"AO Angola 30809762"
]
},
- "execution_count": 905,
+ "execution_count": 871,
"metadata": {},
"output_type": "execute_result"
}
@@ -811,16 +840,16 @@
},
{
"cell_type": "code",
- "execution_count": 906,
+ "execution_count": 872,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(202, 2)"
+ "(204, 2)"
]
},
- "execution_count": 906,
+ "execution_count": 872,
"metadata": {},
"output_type": "execute_result"
}
@@ -831,7 +860,7 @@
},
{
"cell_type": "code",
- "execution_count": 907,
+ "execution_count": 873,
"metadata": {},
"outputs": [
{
@@ -880,7 +909,7 @@
"FI Finland 5518050"
]
},
- "execution_count": 907,
+ "execution_count": 873,
"metadata": {},
"output_type": "execute_result"
}
@@ -891,7 +920,7 @@
},
{
"cell_type": "code",
- "execution_count": 908,
+ "execution_count": 874,
"metadata": {},
"outputs": [
{
@@ -946,11 +975,6 @@
" 4853506 | \n",
" \n",
" \n",
- " SI | \n",
- " Slovenia | \n",
- " 2067372 | \n",
- "
\n",
- " \n",
" ES | \n",
" Spain | \n",
" 46723749 | \n",
@@ -961,6 +985,11 @@
" 82927922 | \n",
"
\n",
" \n",
+ " SI | \n",
+ " Slovenia | \n",
+ " 2067372 | \n",
+ "
\n",
+ " \n",
" BE | \n",
" Belgium | \n",
" 11422068 | \n",
@@ -981,14 +1010,14 @@
"FR France 66987244\n",
"UK United_Kingdom 66488991\n",
"IE Ireland 4853506\n",
- "SI Slovenia 2067372\n",
"ES Spain 46723749\n",
"DE Germany 82927922\n",
+ "SI Slovenia 2067372\n",
"BE Belgium 11422068\n",
"MX Mexico 126190788"
]
},
- "execution_count": 908,
+ "execution_count": 874,
"metadata": {},
"output_type": "execute_result"
}
@@ -999,7 +1028,7 @@
},
{
"cell_type": "code",
- "execution_count": 909,
+ "execution_count": 875,
"metadata": {},
"outputs": [
{
@@ -1075,7 +1104,7 @@
" 2020-03-18 0 0"
]
},
- "execution_count": 909,
+ "execution_count": 875,
"metadata": {},
"output_type": "execute_result"
}
@@ -1087,7 +1116,7 @@
},
{
"cell_type": "code",
- "execution_count": 910,
+ "execution_count": 876,
"metadata": {},
"outputs": [
{
@@ -1152,33 +1181,33 @@
" ... | \n",
"
\n",
" \n",
- " 2020-04-25 | \n",
- " 5386 | \n",
- " 768 | \n",
+ " 2020-05-04 | \n",
+ " 4339 | \n",
+ " 315 | \n",
"
\n",
" \n",
- " 2020-04-26 | \n",
- " 4913 | \n",
- " 813 | \n",
+ " 2020-05-05 | \n",
+ " 3985 | \n",
+ " 288 | \n",
"
\n",
" \n",
- " 2020-04-27 | \n",
- " 4463 | \n",
- " 413 | \n",
+ " 2020-05-06 | \n",
+ " 4406 | \n",
+ " 693 | \n",
"
\n",
" \n",
- " 2020-04-28 | \n",
- " 4309 | \n",
- " 360 | \n",
+ " 2020-05-07 | \n",
+ " 6211 | \n",
+ " 649 | \n",
"
\n",
" \n",
- " 2020-04-29 | \n",
- " 3996 | \n",
- " 586 | \n",
+ " 2020-05-08 | \n",
+ " 5514 | \n",
+ " 539 | \n",
"
\n",
" \n",
"\n",
- "121 rows à 2 columns
\n",
+ "130 rows à 2 columns
\n",
""
],
"text/plain": [
@@ -1190,16 +1219,16 @@
"2020-01-03 0 0\n",
"2020-01-04 0 0\n",
"... ... ...\n",
- "2020-04-25 5386 768\n",
- "2020-04-26 4913 813\n",
- "2020-04-27 4463 413\n",
- "2020-04-28 4309 360\n",
- "2020-04-29 3996 586\n",
+ "2020-05-04 4339 315\n",
+ "2020-05-05 3985 288\n",
+ "2020-05-06 4406 693\n",
+ "2020-05-07 6211 649\n",
+ "2020-05-08 5514 539\n",
"\n",
- "[121 rows x 2 columns]"
+ "[130 rows x 2 columns]"
]
},
- "execution_count": 910,
+ "execution_count": 876,
"metadata": {},
"output_type": "execute_result"
}
@@ -1210,7 +1239,7 @@
},
{
"cell_type": "code",
- "execution_count": 911,
+ "execution_count": 877,
"metadata": {},
"outputs": [
{
@@ -1280,33 +1309,33 @@
" \n",
" \n",
" ZW | \n",
- " 2020-04-25 | \n",
- " 29 | \n",
+ " 2020-05-04 | \n",
+ " 34 | \n",
" 4 | \n",
"
\n",
" \n",
- " 2020-04-26 | \n",
- " 31 | \n",
+ " 2020-05-05 | \n",
+ " 34 | \n",
" 4 | \n",
"
\n",
" \n",
- " 2020-04-27 | \n",
- " 31 | \n",
+ " 2020-05-06 | \n",
+ " 34 | \n",
" 4 | \n",
"
\n",
" \n",
- " 2020-04-28 | \n",
- " 32 | \n",
+ " 2020-05-07 | \n",
+ " 34 | \n",
" 4 | \n",
"
\n",
" \n",
- " 2020-04-29 | \n",
- " 32 | \n",
+ " 2020-05-08 | \n",
+ " 34 | \n",
" 4 | \n",
"
\n",
" \n",
"\n",
- "13829 rows à 2 columns
\n",
+ "15698 rows à 2 columns
\n",
""
],
"text/plain": [
@@ -1318,16 +1347,16 @@
" 2020-03-17 14 0\n",
" 2020-03-18 14 0\n",
"... ... ...\n",
- "ZW 2020-04-25 29 4\n",
- " 2020-04-26 31 4\n",
- " 2020-04-27 31 4\n",
- " 2020-04-28 32 4\n",
- " 2020-04-29 32 4\n",
+ "ZW 2020-05-04 34 4\n",
+ " 2020-05-05 34 4\n",
+ " 2020-05-06 34 4\n",
+ " 2020-05-07 34 4\n",
+ " 2020-05-08 34 4\n",
"\n",
- "[13829 rows x 2 columns]"
+ "[15698 rows x 2 columns]"
]
},
- "execution_count": 911,
+ "execution_count": 877,
"metadata": {},
"output_type": "execute_result"
}
@@ -1338,7 +1367,7 @@
},
{
"cell_type": "code",
- "execution_count": 912,
+ "execution_count": 878,
"metadata": {},
"outputs": [
{
@@ -1424,43 +1453,43 @@
" \n",
" \n",
" ZW | \n",
- " 2020-04-25 | \n",
+ " 2020-05-04 | \n",
" 0 | \n",
- " 1 | \n",
- " 29 | \n",
+ " 0 | \n",
+ " 34 | \n",
" 4 | \n",
"
\n",
" \n",
- " 2020-04-26 | \n",
- " 2 | \n",
+ " 2020-05-05 | \n",
" 0 | \n",
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- "13829 rows à 4 columns
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+ "15698 rows à 4 columns
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""
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@@ -1472,16 +1501,16 @@
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"... ... ... ... ...\n",
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+ "ZW 2020-05-04 0 0 34 4\n",
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+ " 2020-05-06 0 0 34 4\n",
+ " 2020-05-07 0 0 34 4\n",
+ " 2020-05-08 0 0 34 4\n",
"\n",
- "[13829 rows x 4 columns]"
+ "[15698 rows x 4 columns]"
]
},
- "execution_count": 912,
+ "execution_count": 878,
"metadata": {},
"output_type": "execute_result"
}
@@ -1496,81 +1525,7 @@
},
{
"cell_type": "code",
- "execution_count": 913,
- "metadata": {},
- "outputs": [],
- "source": [
- "# data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 914,
- "metadata": {},
- "outputs": [],
- "source": [
- "# days_since_threshold = data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD].groupby(level=0).cumcount()\n",
- "# days_since_threshold.rename('since_threshold', inplace=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 915,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "geoId dateRep \n",
- "AD 2020-04-01 0\n",
- " 2020-04-02 1\n",
- " 2020-04-03 2\n",
- " 2020-04-04 3\n",
- " 2020-04-05 4\n",
- " ..\n",
- "ZA 2020-04-25 19\n",
- " 2020-04-26 20\n",
- " 2020-04-27 21\n",
- " 2020-04-28 22\n",
- " 2020-04-29 23\n",
- "Name: since_threshold, Length: 3082, dtype: int64"
- ]
- },
- "execution_count": 915,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "dbd = data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD].reset_index(level=1)\n",
- "dbd['since_threshold'] = dbd.dateRep\n",
- "dbd.set_index('dateRep', append=True, inplace=True)\n",
- "dbd.sort_index(inplace=True)\n",
- "days_since_threshold = dbd.groupby(level=0).diff().since_threshold.dt.days.fillna(0).astype(int).groupby(level=0).cumsum()\n",
- "# days_since_threshold.groupby(level=0).cumsum()\n",
- "\n",
- "# days_since_threshold = dbd.rename('since_threshold')\n",
- "days_since_threshold"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 916,
- "metadata": {},
- "outputs": [],
- "source": [
- "# days_since_threshold = (data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD]\n",
- "# .reset_index(level=1).groupby(level=0)\n",
- "# .diff().dateRep.dt.days\n",
- "# .groupby(level=0).cumcount()\n",
- "# )\n",
- "# days_since_threshold.rename('since_threshold', inplace=True)\n",
- "# days_since_threshold"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 917,
+ "execution_count": 879,
"metadata": {},
"outputs": [
{
@@ -1599,7 +1554,8 @@
" deaths | \n",
" cases_culm | \n",
" deaths_culm | \n",
- " since_threshold | \n",
+ " cases_diff | \n",
+ " deaths_diff | \n",
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" geoId | \n",
@@ -1609,49 +1565,55 @@
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@@ -1661,72 +1623,578 @@
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+ "15698 rows à 6 columns
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+ ],
+ "text/plain": [
+ " cases deaths cases_culm deaths_culm cases_diff \\\n",
+ "geoId dateRep \n",
+ "AD 2020-03-03 1 0 1 0 NaN \n",
+ " 2020-03-14 1 0 2 0 0.0 \n",
+ " 2020-03-16 3 0 5 0 2.0 \n",
+ " 2020-03-17 9 0 14 0 6.0 \n",
+ " 2020-03-18 0 0 14 0 -9.0 \n",
+ "... ... ... ... ... ... \n",
+ "ZW 2020-05-04 0 0 34 4 0.0 \n",
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+ " 2020-05-07 0 0 34 4 0.0 \n",
+ " 2020-05-08 0 0 34 4 0.0 \n",
+ "\n",
+ " deaths_diff \n",
+ "geoId dateRep \n",
+ "AD 2020-03-03 NaN \n",
+ " 2020-03-14 0.0 \n",
+ " 2020-03-16 0.0 \n",
+ " 2020-03-17 0.0 \n",
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+ "... ... \n",
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+ " 2020-05-07 0.0 \n",
+ " 2020-05-08 0.0 \n",
+ "\n",
+ "[15698 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 879,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_by_date = data_by_date.merge(\n",
+ " data_by_date[['cases', 'deaths']].groupby(level=0).diff(), \n",
+ " suffixes=('', '_diff'), \n",
+ " left_index=True, right_index=True)\n",
+ "data_by_date"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 880,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "cases 4617.0\n",
+ "deaths 1029.0\n",
+ "cases_culm 103093.0\n",
+ "deaths_culm 15944.0\n",
+ "cases_diff 14.0\n",
+ "deaths_diff 187.0\n",
+ "Name: (UK, 2020-04-17 00:00:00), dtype: float64"
+ ]
+ },
+ "execution_count": 880,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_by_date.loc['UK', '2020-04-17']"
+ ]
+ },
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+ "execution_count": 881,
+ "metadata": {},
+ "outputs": [
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+ "\n",
+ "[130 rows x 6 columns]"
+ ]
+ },
+ "execution_count": 881,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "data_by_date.loc['UK']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 882,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 883,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# days_since_threshold = data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD].groupby(level=0).cumcount()\n",
+ "# days_since_threshold.rename('since_threshold', inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 884,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "geoId dateRep \n",
+ "AD 2020-04-01 0\n",
+ " 2020-04-02 1\n",
+ " 2020-04-03 2\n",
+ " 2020-04-04 3\n",
+ " 2020-04-05 4\n",
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+ " 2020-05-07 31\n",
+ " 2020-05-08 32\n",
+ "Name: since_threshold, Length: 4068, dtype: int64"
+ ]
+ },
+ "execution_count": 884,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dbd = data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD].reset_index(level=1)\n",
+ "dbd['since_threshold'] = dbd.dateRep\n",
+ "dbd.set_index('dateRep', append=True, inplace=True)\n",
+ "dbd.sort_index(inplace=True)\n",
+ "days_since_threshold = dbd.groupby(level=0).diff().since_threshold.dt.days.fillna(0).astype(int).groupby(level=0).cumsum()\n",
+ "# days_since_threshold.groupby(level=0).cumsum()\n",
+ "\n",
+ "# days_since_threshold = dbd.rename('since_threshold')\n",
+ "days_since_threshold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 885,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# days_since_threshold = (data_by_date[data_by_date.deaths_culm > DEATH_COUNT_THRESHOLD]\n",
+ "# .reset_index(level=1).groupby(level=0)\n",
+ "# .diff().dateRep.dt.days\n",
+ "# .groupby(level=0).cumcount()\n",
+ "# )\n",
+ "# days_since_threshold.rename('since_threshold', inplace=True)\n",
+ "# days_since_threshold"
+ ]
+ },
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+ "cell_type": "code",
+ "execution_count": 886,
+ "metadata": {},
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\n",
- "
3082 rows à 5 columns
\n",
+ "
4068 rows à 7 columns
\n",
"
"
],
"text/plain": [
- " cases deaths cases_culm deaths_culm since_threshold\n",
- "geoId dateRep \n",
- "AD 2020-04-01 6 4 376 12 0\n",
- " 2020-04-02 14 2 390 14 1\n",
- " 2020-04-03 38 1 428 15 2\n",
- " 2020-04-04 11 1 439 16 3\n",
- " 2020-04-05 27 1 466 17 4\n",
- "... ... ... ... ... ...\n",
- "ZA 2020-04-25 267 4 4220 79 19\n",
- " 2020-04-26 141 7 4361 86 20\n",
- " 2020-04-27 185 1 4546 87 21\n",
- " 2020-04-28 247 3 4793 90 22\n",
- " 2020-04-29 203 3 4996 93 23\n",
+ " cases deaths cases_culm deaths_culm cases_diff \\\n",
+ "geoId dateRep \n",
+ "AD 2020-04-01 6 4 376 12 -30.0 \n",
+ " 2020-04-02 14 2 390 14 8.0 \n",
+ " 2020-04-03 38 1 428 15 24.0 \n",
+ " 2020-04-04 11 1 439 16 -27.0 \n",
+ " 2020-04-05 27 1 466 17 16.0 \n",
+ "... ... ... ... ... ... \n",
+ "ZA 2020-05-04 447 8 6783 131 62.0 \n",
+ " 2020-05-05 437 7 7220 138 -10.0 \n",
+ " 2020-05-06 352 10 7572 148 -85.0 \n",
+ " 2020-05-07 236 5 7808 153 -116.0 \n",
+ " 2020-05-08 424 8 8232 161 188.0 \n",
"\n",
- "[3082 rows x 5 columns]"
+ " deaths_diff since_threshold \n",
+ "geoId dateRep \n",
+ "AD 2020-04-01 2.0 0 \n",
+ " 2020-04-02 -2.0 1 \n",
+ " 2020-04-03 -1.0 2 \n",
+ " 2020-04-04 0.0 3 \n",
+ " 2020-04-05 0.0 4 \n",
+ "... ... ... \n",
+ "ZA 2020-05-04 1.0 28 \n",
+ " 2020-05-05 -1.0 29 \n",
+ " 2020-05-06 3.0 30 \n",
+ " 2020-05-07 -5.0 31 \n",
+ " 2020-05-08 3.0 32 \n",
+ "\n",
+ "[4068 rows x 7 columns]"
]
},
- "execution_count": 917,
+ "execution_count": 886,
"metadata": {},
"output_type": "execute_result"
}
@@ -1739,7 +2207,7 @@
},
{
"cell_type": "code",
- "execution_count": 918,
+ "execution_count": 887,
"metadata": {},
"outputs": [
{
@@ -1769,6 +2237,8 @@
" deaths | \n",
" cases_culm | \n",
" deaths_culm | \n",
+ " cases_diff | \n",
+ " deaths_diff | \n",
" \n",
" \n",
" since_threshold | \n",
@@ -1778,6 +2248,8 @@
" | \n",
" | \n",
" | \n",
+ " | \n",
+ " | \n",
"
\n",
" \n",
" \n",
@@ -1789,6 +2261,8 @@
" 4 | \n",
" 376 | \n",
" 12 | \n",
+ " -30.0 | \n",
+ " 2.0 | \n",
" \n",
" \n",
" 1 | \n",
@@ -1798,6 +2272,8 @@
" 2 | \n",
" 390 | \n",
" 14 | \n",
+ " 8.0 | \n",
+ " -2.0 | \n",
"
\n",
" \n",
" 2 | \n",
@@ -1807,6 +2283,8 @@
" 1 | \n",
" 428 | \n",
" 15 | \n",
+ " 24.0 | \n",
+ " -1.0 | \n",
"
\n",
" \n",
" 3 | \n",
@@ -1816,6 +2294,8 @@
" 1 | \n",
" 439 | \n",
" 16 | \n",
+ " -27.0 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 4 | \n",
@@ -1825,6 +2305,8 @@
" 1 | \n",
" 466 | \n",
" 17 | \n",
+ " 16.0 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" ... | \n",
@@ -1834,76 +2316,102 @@
" ... | \n",
" ... | \n",
" ... | \n",
+ " ... | \n",
+ " ... | \n",
"
\n",
" \n",
- " 19 | \n",
+ " 28 | \n",
" ZA | \n",
- " 2020-04-25 | \n",
- " 267 | \n",
- " 4 | \n",
- " 4220 | \n",
- " 79 | \n",
+ " 2020-05-04 | \n",
+ " 447 | \n",
+ " 8 | \n",
+ " 6783 | \n",
+ " 131 | \n",
+ " 62.0 | \n",
+ " 1.0 | \n",
"
\n",
" \n",
- " 20 | \n",
+ " 29 | \n",
" ZA | \n",
- " 2020-04-26 | \n",
- " 141 | \n",
+ " 2020-05-05 | \n",
+ " 437 | \n",
" 7 | \n",
- " 4361 | \n",
- " 86 | \n",
+ " 7220 | \n",
+ " 138 | \n",
+ " -10.0 | \n",
+ " -1.0 | \n",
"
\n",
" \n",
- " 21 | \n",
+ " 30 | \n",
" ZA | \n",
- " 2020-04-27 | \n",
- " 185 | \n",
- " 1 | \n",
- " 4546 | \n",
- " 87 | \n",
+ " 2020-05-06 | \n",
+ " 352 | \n",
+ " 10 | \n",
+ " 7572 | \n",
+ " 148 | \n",
+ " -85.0 | \n",
+ " 3.0 | \n",
"
\n",
" \n",
- " 22 | \n",
+ " 31 | \n",
" ZA | \n",
- " 2020-04-28 | \n",
- " 247 | \n",
- " 3 | \n",
- " 4793 | \n",
- " 90 | \n",
+ " 2020-05-07 | \n",
+ " 236 | \n",
+ " 5 | \n",
+ " 7808 | \n",
+ " 153 | \n",
+ " -116.0 | \n",
+ " -5.0 | \n",
"
\n",
" \n",
- " 23 | \n",
+ " 32 | \n",
" ZA | \n",
- " 2020-04-29 | \n",
- " 203 | \n",
- " 3 | \n",
- " 4996 | \n",
- " 93 | \n",
+ " 2020-05-08 | \n",
+ " 424 | \n",
+ " 8 | \n",
+ " 8232 | \n",
+ " 161 | \n",
+ " 188.0 | \n",
+ " 3.0 | \n",
"
\n",
" \n",
"\n",
- "3082 rows à 5 columns
\n",
+ "4068 rows à 7 columns
\n",
""
],
"text/plain": [
- " dateRep cases deaths cases_culm deaths_culm\n",
- "since_threshold geoId \n",
- "0 AD 2020-04-01 6 4 376 12\n",
- "1 AD 2020-04-02 14 2 390 14\n",
- "2 AD 2020-04-03 38 1 428 15\n",
- "3 AD 2020-04-04 11 1 439 16\n",
- "4 AD 2020-04-05 27 1 466 17\n",
- "... ... ... ... ... ...\n",
- "19 ZA 2020-04-25 267 4 4220 79\n",
- "20 ZA 2020-04-26 141 7 4361 86\n",
- "21 ZA 2020-04-27 185 1 4546 87\n",
- "22 ZA 2020-04-28 247 3 4793 90\n",
- "23 ZA 2020-04-29 203 3 4996 93\n",
+ " dateRep cases deaths cases_culm deaths_culm \\\n",
+ "since_threshold geoId \n",
+ "0 AD 2020-04-01 6 4 376 12 \n",
+ "1 AD 2020-04-02 14 2 390 14 \n",
+ "2 AD 2020-04-03 38 1 428 15 \n",
+ "3 AD 2020-04-04 11 1 439 16 \n",
+ "4 AD 2020-04-05 27 1 466 17 \n",
+ "... ... ... ... ... ... \n",
+ "28 ZA 2020-05-04 447 8 6783 131 \n",
+ "29 ZA 2020-05-05 437 7 7220 138 \n",
+ "30 ZA 2020-05-06 352 10 7572 148 \n",
+ "31 ZA 2020-05-07 236 5 7808 153 \n",
+ "32 ZA 2020-05-08 424 8 8232 161 \n",
"\n",
- "[3082 rows x 5 columns]"
+ " cases_diff deaths_diff \n",
+ "since_threshold geoId \n",
+ "0 AD -30.0 2.0 \n",
+ "1 AD 8.0 -2.0 \n",
+ "2 AD 24.0 -1.0 \n",
+ "3 AD -27.0 0.0 \n",
+ "4 AD 16.0 0.0 \n",
+ "... ... ... \n",
+ "28 ZA 62.0 1.0 \n",
+ "29 ZA -10.0 -1.0 \n",
+ "30 ZA -85.0 3.0 \n",
+ "31 ZA -116.0 -5.0 \n",
+ "32 ZA 188.0 3.0 \n",
+ "\n",
+ "[4068 rows x 7 columns]"
]
},
- "execution_count": 918,
+ "execution_count": 887,
"metadata": {},
"output_type": "execute_result"
}
@@ -1917,7 +2425,7 @@
},
{
"cell_type": "code",
- "execution_count": 919,
+ "execution_count": 888,
"metadata": {},
"outputs": [
{
@@ -1947,6 +2455,8 @@
" deaths | \n",
" cases_culm | \n",
" deaths_culm | \n",
+ " cases_diff | \n",
+ " deaths_diff | \n",
" \n",
" \n",
" since_threshold | \n",
@@ -1956,6 +2466,8 @@
" | \n",
" | \n",
" | \n",
+ " | \n",
+ " | \n",
"
\n",
" \n",
" \n",
@@ -1967,6 +2479,8 @@
" 4 | \n",
" 4838 | \n",
" 12 | \n",
+ " 310.0 | \n",
+ " 1.0 | \n",
" \n",
" \n",
" 1 | \n",
@@ -1976,6 +2490,8 @@
" 1 | \n",
" 6012 | \n",
" 13 | \n",
+ " 131.0 | \n",
+ " -3.0 | \n",
"
\n",
" \n",
" 2 | \n",
@@ -1985,6 +2501,8 @@
" 0 | \n",
" 7156 | \n",
" 13 | \n",
+ " -30.0 | \n",
+ " -1.0 | \n",
"
\n",
" \n",
" 3 | \n",
@@ -1994,6 +2512,8 @@
" 0 | \n",
" 8198 | \n",
" 13 | \n",
+ " -102.0 | \n",
+ " 0.0 | \n",
"
\n",
" \n",
" 4 | \n",
@@ -2003,6 +2523,8 @@
" 30 | \n",
" 14138 | \n",
" 43 | \n",
+ " 4898.0 | \n",
+ " 30.0 | \n",
"
\n",
" \n",
" ... | \n",
@@ -2012,76 +2534,102 @@
" ... | \n",
" ... | \n",
" ... | \n",
+ " ... | \n",
+ " ... | \n",
"
\n",
" \n",
- " 41 | \n",
+ " 50 | \n",
" UK | \n",
- " 2020-04-25 | \n",
- " 5386 | \n",
- " 768 | \n",
- " 143464 | \n",
- " 19506 | \n",
+ " 2020-05-04 | \n",
+ " 4339 | \n",
+ " 315 | \n",
+ " 186599 | \n",
+ " 28446 | \n",
+ " -467.0 | \n",
+ " -306.0 | \n",
"
\n",
" \n",
- " 42 | \n",
+ " 51 | \n",
" UK | \n",
- " 2020-04-26 | \n",
- " 4913 | \n",
- " 813 | \n",
- " 148377 | \n",
- " 20319 | \n",
+ " 2020-05-05 | \n",
+ " 3985 | \n",
+ " 288 | \n",
+ " 190584 | \n",
+ " 28734 | \n",
+ " -354.0 | \n",
+ " -27.0 | \n",
"
\n",
" \n",
- " 43 | \n",
+ " 52 | \n",
" UK | \n",
- " 2020-04-27 | \n",
- " 4463 | \n",
- " 413 | \n",
- " 152840 | \n",
- " 20732 | \n",
+ " 2020-05-06 | \n",
+ " 4406 | \n",
+ " 693 | \n",
+ " 194990 | \n",
+ " 29427 | \n",
+ " 421.0 | \n",
+ " 405.0 | \n",
"
\n",
" \n",
- " 44 | \n",
+ " 53 | \n",
" UK | \n",
- " 2020-04-28 | \n",
- " 4309 | \n",
- " 360 | \n",
- " 157149 | \n",
- " 21092 | \n",
+ " 2020-05-07 | \n",
+ " 6211 | \n",
+ " 649 | \n",
+ " 201201 | \n",
+ " 30076 | \n",
+ " 1805.0 | \n",
+ " -44.0 | \n",
"
\n",
" \n",
- " 45 | \n",
+ " 54 | \n",
" UK | \n",
- " 2020-04-29 | \n",
- " 3996 | \n",
- " 586 | \n",
- " 161145 | \n",
- " 21678 | \n",
+ " 2020-05-08 | \n",
+ " 5514 | \n",
+ " 539 | \n",
+ " 206715 | \n",
+ " 30615 | \n",
+ " -697.0 | \n",
+ " -110.0 | \n",
"
\n",
" \n",
"\n",
- "155 rows à 5 columns
\n",
+ "182 rows à 7 columns
\n",
""
],
"text/plain": [
- " dateRep cases deaths cases_culm deaths_culm\n",
- "since_threshold geoId \n",
- "0 DE 2020-03-16 1043 4 4838 12\n",
- "1 DE 2020-03-17 1174 1 6012 13\n",
- "2 DE 2020-03-18 1144 0 7156 13\n",
- "3 DE 2020-03-19 1042 0 8198 13\n",
- "4 DE 2020-03-20 5940 30 14138 43\n",
- "... ... ... ... ... ...\n",
- "41 UK 2020-04-25 5386 768 143464 19506\n",
- "42 UK 2020-04-26 4913 813 148377 20319\n",
- "43 UK 2020-04-27 4463 413 152840 20732\n",
- "44 UK 2020-04-28 4309 360 157149 21092\n",
- "45 UK 2020-04-29 3996 586 161145 21678\n",
+ " dateRep cases deaths cases_culm deaths_culm \\\n",
+ "since_threshold geoId \n",
+ "0 DE 2020-03-16 1043 4 4838 12 \n",
+ "1 DE 2020-03-17 1174 1 6012 13 \n",
+ "2 DE 2020-03-18 1144 0 7156 13 \n",
+ "3 DE 2020-03-19 1042 0 8198 13 \n",
+ "4 DE 2020-03-20 5940 30 14138 43 \n",
+ "... ... ... ... ... ... \n",
+ "50 UK 2020-05-04 4339 315 186599 28446 \n",
+ "51 UK 2020-05-05 3985 288 190584 28734 \n",
+ "52 UK 2020-05-06 4406 693 194990 29427 \n",
+ "53 UK 2020-05-07 6211 649 201201 30076 \n",
+ "54 UK 2020-05-08 5514 539 206715 30615 \n",
"\n",
- "[155 rows x 5 columns]"
+ " cases_diff deaths_diff \n",
+ "since_threshold geoId \n",
+ "0 DE 310.0 1.0 \n",
+ "1 DE 131.0 -3.0 \n",
+ "2 DE -30.0 -1.0 \n",
+ "3 DE -102.0 0.0 \n",
+ "4 DE 4898.0 30.0 \n",
+ "... ... ... \n",
+ "50 UK -467.0 -306.0 \n",
+ "51 UK -354.0 -27.0 \n",
+ "52 UK 421.0 405.0 \n",
+ "53 UK 1805.0 -44.0 \n",
+ "54 UK -697.0 -110.0 \n",
+ "\n",
+ "[182 rows x 7 columns]"
]
},
- "execution_count": 919,
+ "execution_count": 888,
"metadata": {},
"output_type": "execute_result"
}
@@ -2092,22 +2640,22 @@
},
{
"cell_type": "code",
- "execution_count": 920,
+ "execution_count": 889,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 920,
+ "execution_count": 889,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
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