diff --git a/codigo/trabajo.ipynb b/codigo/trabajo.ipynb
index f5002bf0a293746b4b74e603f267e65ca5d48270..d6d43c575e88d14fbbecc0c5adb647cfa8fb9ddb 100644
--- a/codigo/trabajo.ipynb
+++ b/codigo/trabajo.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 62,
"metadata": {
"scrolled": true
},
@@ -12,12 +12,13 @@
"import csv\n",
"import numpy as np\n",
"import matplotlib as plt\n",
- "import seaborn as sns"
+ "import seaborn as sns\n",
+ "import math"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
@@ -27,61 +28,487 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pip install --upgrade pip"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
+ "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>species_id</th>\n",
+ " <th>genus</th>\n",
+ " <th>species</th>\n",
+ " <th>taxa</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>AB</td>\n",
+ " <td>Amphispiza</td>\n",
+ " <td>bilineata</td>\n",
+ " <td>Bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>AH</td>\n",
+ " <td>Ammospermophilus</td>\n",
+ " <td>harrisi</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>AS</td>\n",
+ " <td>Ammodramus</td>\n",
+ " <td>savannarum</td>\n",
+ " <td>Bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>BA</td>\n",
+ " <td>Baiomys</td>\n",
+ " <td>taylori</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>CB</td>\n",
+ " <td>Campylorhynchus</td>\n",
+ " <td>brunneicapillus</td>\n",
+ " <td>Bird</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " species_id genus species taxa\n",
+ "0 AB Amphispiza bilineata Bird\n",
+ "1 AH Ammospermophilus harrisi Rodent\n",
+ "2 AS Ammodramus savannarum Bird\n",
+ "3 BA Baiomys taylori Rodent\n",
+ "4 CB Campylorhynchus brunneicapillus Bird"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"df_species.head()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
+ "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>record_id</th>\n",
+ " <th>month</th>\n",
+ " <th>day</th>\n",
+ " <th>year</th>\n",
+ " <th>plot_id</th>\n",
+ " <th>species_id</th>\n",
+ " <th>sex</th>\n",
+ " <th>hindfoot_length</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>2</td>\n",
+ " <td>NL</td>\n",
+ " <td>M</td>\n",
+ " <td>32.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
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+ " <td>3</td>\n",
+ " <td>NL</td>\n",
+ " <td>M</td>\n",
+ " <td>33.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>2</td>\n",
+ " <td>DM</td>\n",
+ " <td>F</td>\n",
+ " <td>37.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>7</td>\n",
+ " <td>DM</td>\n",
+ " <td>M</td>\n",
+ " <td>36.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>3</td>\n",
+ " <td>DM</td>\n",
+ " <td>M</td>\n",
+ " <td>35.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " record_id month day year plot_id species_id sex hindfoot_length \\\n",
+ "0 1 7 16 1977 2 NL M 32.0 \n",
+ "1 2 7 16 1977 3 NL M 33.0 \n",
+ "2 3 7 16 1977 2 DM F 37.0 \n",
+ "3 4 7 16 1977 7 DM M 36.0 \n",
+ "4 5 7 16 1977 3 DM M 35.0 \n",
+ "\n",
+ " weight \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN "
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"df_surveys.head()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .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>record_id</th>\n",
+ " <th>month</th>\n",
+ " <th>day</th>\n",
+ " <th>year</th>\n",
+ " <th>plot_id</th>\n",
+ " <th>species_id</th>\n",
+ " <th>sex</th>\n",
+ " <th>hindfoot_length</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
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+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
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+ " <tr>\n",
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+ " <td>M</td>\n",
+ " <td>33.0</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
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+ " <td>F</td>\n",
+ " <td>37.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>7</td>\n",
+ " <td>DM</td>\n",
+ " <td>M</td>\n",
+ " <td>36.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>7</td>\n",
+ " <td>16</td>\n",
+ " <td>1977</td>\n",
+ " <td>3</td>\n",
+ " <td>DM</td>\n",
+ " <td>M</td>\n",
+ " <td>35.0</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35544</th>\n",
+ " <td>35545</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>15</td>\n",
+ " <td>AH</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35545</th>\n",
+ " <td>35546</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>15</td>\n",
+ " <td>AH</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35546</th>\n",
+ " <td>35547</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>10</td>\n",
+ " <td>RM</td>\n",
+ " <td>F</td>\n",
+ " <td>15.0</td>\n",
+ " <td>14.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35547</th>\n",
+ " <td>35548</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>7</td>\n",
+ " <td>DO</td>\n",
+ " <td>M</td>\n",
+ " <td>36.0</td>\n",
+ " <td>51.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35548</th>\n",
+ " <td>35549</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>5</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>35549 rows × 9 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " record_id month day year plot_id species_id sex hindfoot_length \\\n",
+ "0 1 7 16 1977 2 NL M 32.0 \n",
+ "1 2 7 16 1977 3 NL M 33.0 \n",
+ "2 3 7 16 1977 2 DM F 37.0 \n",
+ "3 4 7 16 1977 7 DM M 36.0 \n",
+ "4 5 7 16 1977 3 DM M 35.0 \n",
+ "... ... ... ... ... ... ... ... ... \n",
+ "35544 35545 12 31 2002 15 AH NaN NaN \n",
+ "35545 35546 12 31 2002 15 AH NaN NaN \n",
+ "35546 35547 12 31 2002 10 RM F 15.0 \n",
+ "35547 35548 12 31 2002 7 DO M 36.0 \n",
+ "35548 35549 12 31 2002 5 NaN NaN NaN \n",
+ "\n",
+ " weight \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "... ... \n",
+ "35544 NaN \n",
+ "35545 NaN \n",
+ "35546 14.0 \n",
+ "35547 51.0 \n",
+ "35548 NaN \n",
+ "\n",
+ "[35549 rows x 9 columns]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"df_surveys"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['AB', 'AH', 'AS', 'BA', 'CB', 'CM', 'CQ', 'CS', 'CT', 'CU', 'CV',\n",
+ " 'DM', 'DO', 'DS', 'DX', 'EO', 'GS', 'NL', 'NX', 'OL', 'OT', 'OX',\n",
+ " 'PB', 'PC', 'PE', 'PF', 'PG', 'PH', 'PI', 'PL', 'PM', 'PP', 'PU',\n",
+ " 'PX', 'RF', 'RM', 'RO', 'RX', 'SA', 'SB', 'SC', 'SF', 'SH', 'SO',\n",
+ " 'SS', 'ST', 'SU', 'SX', 'UL', 'UP', 'UR', 'US', 'ZL', 'ZM'],\n",
+ " dtype=object)"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"df_species['species_id'].unique()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['NL', 'DM', 'PF', 'PE', 'DS', 'PP', 'SH', 'OT', 'DO', 'OX', 'SS',\n",
+ " 'OL', 'RM', nan, 'SA', 'PM', 'AH', 'DX', 'AB', 'CB', 'CM', 'CQ',\n",
+ " 'RF', 'PC', 'PG', 'PH', 'PU', 'CV', 'UR', 'UP', 'ZL', 'UL', 'CS',\n",
+ " 'SC', 'BA', 'SF', 'RO', 'AS', 'SO', 'PI', 'ST', 'CU', 'SU', 'RX',\n",
+ " 'PB', 'PL', 'PX', 'CT', 'US'], dtype=object)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"df_surveys['species_id'].unique()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -90,30 +517,295 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>record_id</th>\n",
+ " <th>month</th>\n",
+ " <th>day</th>\n",
+ " <th>year</th>\n",
+ " <th>plot_id</th>\n",
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+ " <th>sex</th>\n",
+ " <th>hindfoot_length</th>\n",
+ " <th>weight</th>\n",
+ " <th>genus</th>\n",
+ " <th>species</th>\n",
+ " <th>taxa</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
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+ " <td>NL</td>\n",
+ " <td>M</td>\n",
+ " <td>32.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Neotoma</td>\n",
+ " <td>albigula</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
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+ " <td>Neotoma</td>\n",
+ " <td>albigula</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
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+ " <th>2</th>\n",
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+ " <td>DM</td>\n",
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+ " <td>37.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Dipodomys</td>\n",
+ " <td>merriami</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
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+ " <td>16</td>\n",
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+ " </tr>\n",
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+ " <th>4</th>\n",
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+ " <td>1977</td>\n",
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+ " <td>M</td>\n",
+ " <td>35.0</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Dipodomys</td>\n",
+ " <td>merriami</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
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+ " <td>...</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th>35544</th>\n",
+ " <td>35545</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>15</td>\n",
+ " <td>AH</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Ammospermophilus</td>\n",
+ " <td>harrisi</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35545</th>\n",
+ " <td>35546</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>15</td>\n",
+ " <td>AH</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>Ammospermophilus</td>\n",
+ " <td>harrisi</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35546</th>\n",
+ " <td>35547</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>10</td>\n",
+ " <td>RM</td>\n",
+ " <td>F</td>\n",
+ " <td>15.0</td>\n",
+ " <td>14.0</td>\n",
+ " <td>Reithrodontomys</td>\n",
+ " <td>megalotis</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35547</th>\n",
+ " <td>35548</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>7</td>\n",
+ " <td>DO</td>\n",
+ " <td>M</td>\n",
+ " <td>36.0</td>\n",
+ " <td>51.0</td>\n",
+ " <td>Dipodomys</td>\n",
+ " <td>ordii</td>\n",
+ " <td>Rodent</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35548</th>\n",
+ " <td>35549</td>\n",
+ " <td>12</td>\n",
+ " <td>31</td>\n",
+ " <td>2002</td>\n",
+ " <td>5</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>35549 rows × 12 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " record_id month day year plot_id species_id sex hindfoot_length \\\n",
+ "0 1 7 16 1977 2 NL M 32.0 \n",
+ "1 2 7 16 1977 3 NL M 33.0 \n",
+ "2 3 7 16 1977 2 DM F 37.0 \n",
+ "3 4 7 16 1977 7 DM M 36.0 \n",
+ "4 5 7 16 1977 3 DM M 35.0 \n",
+ "... ... ... ... ... ... ... ... ... \n",
+ "35544 35545 12 31 2002 15 AH NaN NaN \n",
+ "35545 35546 12 31 2002 15 AH NaN NaN \n",
+ "35546 35547 12 31 2002 10 RM F 15.0 \n",
+ "35547 35548 12 31 2002 7 DO M 36.0 \n",
+ "35548 35549 12 31 2002 5 NaN NaN NaN \n",
+ "\n",
+ " weight genus species taxa \n",
+ "0 NaN Neotoma albigula Rodent \n",
+ "1 NaN Neotoma albigula Rodent \n",
+ "2 NaN Dipodomys merriami Rodent \n",
+ "3 NaN Dipodomys merriami Rodent \n",
+ "4 NaN Dipodomys merriami Rodent \n",
+ "... ... ... ... ... \n",
+ "35544 NaN Ammospermophilus harrisi Rodent \n",
+ "35545 NaN Ammospermophilus harrisi Rodent \n",
+ "35546 14.0 Reithrodontomys megalotis Rodent \n",
+ "35547 51.0 Dipodomys ordii Rodent \n",
+ "35548 NaN NaN NaN NaN \n",
+ "\n",
+ "[35549 rows x 12 columns]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"df_surveysspecie"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 73,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/student/.local/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+ " \"\"\"Entry point for launching an IPython kernel.\n",
+ "/home/student/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+ " \n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x7f12bcedf748>"
+ ]
+ },
+ "execution_count": 73,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "df_group_specie_id = df_surveysspecie.groupby('taxa').unique()\n",
- "df_group_specie_id"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "plt.pyplot.scatter(df_surveysspecie[df_surveysspecie['weight']>0],df_surveysspecie[df_surveysspecie['hindfoot_length']>0]['weight'])"
+ "plt.pyplot.scatter(df_surveysspecie[df_surveysspecie['weight']>0][df_surveysspecie['hindfoot_length']>0][df_surveysspecie['hindfoot_length']<40][df_surveysspecie['taxa']=='Rodent']['weight'],\n",
+ " df_surveysspecie[df_surveysspecie['weight']>0][df_surveysspecie['hindfoot_length']>0][df_surveysspecie['hindfoot_length']<40][df_surveysspecie['taxa']=='Rodent']['hindfoot_length'])"
]
},
{
@@ -199,14 +891,218 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 123,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/student/.local/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+ " \"\"\"Entry point for launching an IPython kernel.\n",
+ "/home/student/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+ " \n",
+ "/home/student/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+ " This is separate from the ipykernel package so we can avoid doing imports until\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]),\n",
+ " <a list of 12 Text xticklabel objects>)"
+ ]
+ },
+ "execution_count": 123,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_weight_vs_taxa= pd.concat([df_surveysspecie[df_surveysspecie['weight']>0][df_surveysspecie['hindfoot_length']>40]['hindfoot_length'],\n",
+ " df_surveysspecie[df_surveysspecie['weight']>0][df_surveysspecie['hindfoot_length']>40]['month'],\n",
+ " df_surveysspecie[df_surveysspecie['weight']>0][df_surveysspecie['hindfoot_length']>40]['sex']],axis=1)\n",
+ "f,ax=plt.pyplot.subplots(figsize=(14,6))\n",
+ "fig = sns.boxplot(x='month',y='hindfoot_length',hue='sex',data=df_weight_vs_taxa)\n",
+ "plt.pyplot.xticks(rotation=90)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "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></th>\n",
+ " <th>record_id</th>\n",
+ " <th>month</th>\n",
+ " <th>day</th>\n",
+ " <th>plot_id</th>\n",
+ " <th>hindfoot_length</th>\n",
+ " <th>weight</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>year</th>\n",
+ " <th>sex</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">1977</th>\n",
+ " <th>F</th>\n",
+ " <td>242.343137</td>\n",
+ " <td>9.480392</td>\n",
+ " <td>15.171569</td>\n",
+ " <td>11.387255</td>\n",
+ " <td>36.574359</td>\n",
+ " <td>47.607692</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>M</th>\n",
+ " <td>228.350467</td>\n",
+ " <td>9.313084</td>\n",
+ " <td>15.364486</td>\n",
+ " <td>11.747664</td>\n",
+ " <td>36.165877</td>\n",
+ " <td>46.135338</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th rowspan=\"2\" valign=\"top\">1978</th>\n",
+ " <th>F</th>\n",
+ " <td>1059.359841</td>\n",
+ " <td>6.910537</td>\n",
+ " <td>8.143141</td>\n",
+ " <td>10.781312</td>\n",
+ " <td>38.303493</td>\n",
+ " <td>69.959052</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>M</th>\n",
+ " <td>1019.023095</td>\n",
+ " <td>6.498845</td>\n",
+ " <td>8.330254</td>\n",
+ " <td>10.501155</td>\n",
+ " <td>36.145729</td>\n",
+ " <td>65.348371</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1979</th>\n",
+ " <th>F</th>\n",
+ " <td>1915.168196</td>\n",
+ " <td>6.688073</td>\n",
+ " <td>22.874618</td>\n",
+ " <td>11.798165</td>\n",
+ " <td>34.871287</td>\n",
+ " <td>65.562500</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " record_id month day plot_id hindfoot_length \\\n",
+ "year sex \n",
+ "1977 F 242.343137 9.480392 15.171569 11.387255 36.574359 \n",
+ " M 228.350467 9.313084 15.364486 11.747664 36.165877 \n",
+ "1978 F 1059.359841 6.910537 8.143141 10.781312 38.303493 \n",
+ " M 1019.023095 6.498845 8.330254 10.501155 36.145729 \n",
+ "1979 F 1915.168196 6.688073 22.874618 11.798165 34.871287 \n",
+ "\n",
+ " weight \n",
+ "year sex \n",
+ "1977 F 47.607692 \n",
+ " M 46.135338 \n",
+ "1978 F 69.959052 \n",
+ " M 65.348371 \n",
+ "1979 F 65.562500 "
+ ]
+ },
+ "execution_count": 107,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_promyear = df_surveysspecie.groupby(['year','sex']).mean()\n",
+ "df_promyear.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f12bb2b38d0>"
+ ]
+ },
+ "execution_count": 121,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "df_weight_vs_taxa= pd.concat([df_surveysspecie[df_surveysspecie['weight']>0]['weight'],df_surveysspecie[df_surveysspecie['weight']>0]['taxa']],axis=1)\n",
- "f,ax=plt.subplots()\n",
- "fig"
+ "sns.scatterplot(x='year',y='hindfoot_length',hue='sex',data=df_promyear)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {