{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finding documentation for python functions is easy:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "?pd.read_csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we read in the population frequency data:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "dat = pd.read_csv(\"population_frequencies.txt\", delim_whitespace=True, names=[\"nr\", \"pop\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and verify that it worked:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | nr | \n", "pop | \n", "
---|---|---|
0 | \n", "9 | \n", "Abkhasian | \n", "
1 | \n", "16 | \n", "Adygei | \n", "
2 | \n", "6 | \n", "Albanian | \n", "
3 | \n", "7 | \n", "Aleut | \n", "
4 | \n", "4 | \n", "Aleut_Tlingit | \n", "
5 | \n", "7 | \n", "Altaian | \n", "
6 | \n", "10 | \n", "Ami | \n", "
7 | \n", "10 | \n", "Armenian | \n", "
8 | \n", "9 | \n", "Atayal | \n", "
9 | \n", "10 | \n", "Balkar | \n", "
10 | \n", "29 | \n", "Basque | \n", "
11 | \n", "25 | \n", "BedouinA | \n", "
12 | \n", "19 | \n", "BedouinB | \n", "
13 | \n", "10 | \n", "Belarusian | \n", "
14 | \n", "6 | \n", "BolshoyOleniOstrov | \n", "
15 | \n", "9 | \n", "Borneo | \n", "
16 | \n", "10 | \n", "Bulgarian | \n", "
17 | \n", "8 | \n", "Cambodian | \n", "
18 | \n", "2 | \n", "Canary_Islander | \n", "
19 | \n", "2 | \n", "ChalmnyVarre | \n", "
20 | \n", "9 | \n", "Chechen | \n", "
21 | \n", "20 | \n", "Chukchi | \n", "
22 | \n", "3 | \n", "Chukchi1 | \n", "
23 | \n", "10 | \n", "Chuvash | \n", "
24 | \n", "10 | \n", "Croatian | \n", "
25 | \n", "8 | \n", "Cypriot | \n", "
26 | \n", "10 | \n", "Czech | \n", "
27 | \n", "10 | \n", "Dai | \n", "
28 | \n", "9 | \n", "Daur | \n", "
29 | \n", "4 | \n", "Dolgan | \n", "
... | \n", "... | \n", "... | \n", "
86 | \n", "27 | \n", "Sardinian | \n", "
87 | \n", "8 | \n", "Saudi | \n", "
88 | \n", "4 | \n", "Scottish | \n", "
89 | \n", "10 | \n", "Selkup | \n", "
90 | \n", "10 | \n", "Semende | \n", "
91 | \n", "10 | \n", "She | \n", "
92 | \n", "2 | \n", "Sherpa.DG | \n", "
93 | \n", "11 | \n", "Sicilian | \n", "
94 | \n", "53 | \n", "Spanish | \n", "
95 | \n", "5 | \n", "Spanish_North | \n", "
96 | \n", "8 | \n", "Syrian | \n", "
97 | \n", "8 | \n", "Tajik | \n", "
98 | \n", "10 | \n", "Thai | \n", "
99 | \n", "2 | \n", "Tibetan.DG | \n", "
100 | \n", "10 | \n", "Tu | \n", "
101 | \n", "22 | \n", "Tubalar | \n", "
102 | \n", "10 | \n", "Tujia | \n", "
103 | \n", "50 | \n", "Turkish | \n", "
104 | \n", "7 | \n", "Turkmen | \n", "
105 | \n", "10 | \n", "Tuvinian | \n", "
106 | \n", "9 | \n", "Ukrainian | \n", "
107 | \n", "25 | \n", "Ulchi | \n", "
108 | \n", "10 | \n", "Uygur | \n", "
109 | \n", "10 | \n", "Uzbek | \n", "
110 | \n", "3 | \n", "WHG | \n", "
111 | \n", "7 | \n", "Xibo | \n", "
112 | \n", "20 | \n", "Yakut | \n", "
113 | \n", "9 | \n", "Yamnaya_Samara | \n", "
114 | \n", "10 | \n", "Yi | \n", "
115 | \n", "19 | \n", "Yukagir | \n", "
116 rows × 2 columns
\n", "