forked from tdwojak/Python2019
1995 lines
53 KiB
Plaintext
1995 lines
53 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"# Podstawy Analizy danych w Pythonie: pandas\n",
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"\n",
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"## 9 lutego 2019"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Ostatnia cześć kursu Pythona będzie dotyczyć biblioteki **pandas**, która służy do analizy danych. Zacznijmy zatem od importu. Przeważnie bibliotekę skraca się do *pd*:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import sys\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"Pandas posiada dwie podstawowe struktury danych: \n",
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" * szereg (Series),\n",
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" * ramka danych (DataFrame). \n",
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" \n",
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"Zaczniemy od szeregów. Szereg danych, mówiąc prościej jest to lista danych tego samego typu."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Żeby zobaczyć szeregi w akcji, stwórzmy listę losowych liczb:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 4 5 10 9 13 7 7 4 19 13 13 10 2 3 17 18 3 14 4 19 5 6 9 5\n",
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" 13 14]\n"
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]
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}
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],
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"source": [
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"losowe = np.random.randint(1, 20, 26)\n",
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"print(losowe)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"A następnie stwórzmy szereg, korzystając z powyższych liczb:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 4\n",
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"1 5\n",
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"2 10\n",
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"3 9\n",
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"4 13\n",
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"5 7\n",
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"6 7\n",
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"7 4\n",
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"8 19\n",
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"9 13\n",
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"10 13\n",
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"11 10\n",
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"12 2\n",
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"13 3\n",
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"14 17\n",
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"15 18\n",
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"16 3\n",
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"17 14\n",
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"18 4\n",
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"19 19\n",
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"20 5\n",
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"21 6\n",
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"22 9\n",
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"23 5\n",
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"24 13\n",
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"25 14\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"dane = pd.Series(losowe)\n",
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"print(dane)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Czym różni się szereg od listy? Szereg danych posiada indeks, czyli klucz, dzięki ktoremu możemy zindetyfikować dane. Domyślnie, indeks jest ciągiem liczb zaczynających się od zera. Nie musi tak być, możemy podczas tworzenia przekazać również indeks:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"a 1\n",
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"b 2\n",
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"c 3\n",
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"d 4\n",
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"e 5\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"dane2 = pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])\n",
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"print(dane2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Jak można domyśleć się, indeks służy nam do uzyskania dostępu do danego elementu:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"2\n"
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]
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}
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],
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"source": [
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"print(dane2['b'])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Więcej o dostępnie do danych będzie w dalszej części kursu."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"Żeby uzyskać rozmiar danych możemy wykorzystać znaną już funkcję **len** lub wykorzystać polę **shape**."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"26\n",
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"(26,)\n"
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]
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}
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],
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"source": [
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"print(len(dane))\n",
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"print(dane.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Przeważnie zbiory danych, na których pracujemy są duże. Stąd, próba ich wyświetlenia może okazać się karkołomna\n",
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"lub nawet niemożliwa. Czasem chcemy tylko zobaczyć pogląd. Do tego służą dwie metody: **head** i **tail**, które\n",
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" zwrócą nam kilka pierwszych lub ostatnich wierszy z szeregu:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 4\n",
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"1 5\n",
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"2 10\n",
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"3 9\n",
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"4 13\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"print(dane.head())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"21 6\n",
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"22 9\n",
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"23 5\n",
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"24 13\n",
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"25 14\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"print(dane.tail())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Szeregi są dostosowane do analizy danych. Np. udostępniają prosty sposób do uzyskania podstawowych statystyk:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Średnia: 9.461538461538462\n",
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"Mediana: 9.0\n"
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]
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}
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],
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"source": [
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"print(\"Średnia:\", dane.mean())\n",
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"print(\"Mediana:\", dane.median())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Jak i inne przydatne funkcje:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Zbiór wartości: [14 6 16 7 17 13 15 2 12 3 4 1 9 19 5]\n",
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"Zliczanie 15 4\n",
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"13 3\n",
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"4 3\n",
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"12 2\n",
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"7 2\n",
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"2 2\n",
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"19 1\n",
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"17 1\n",
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"16 1\n",
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"14 1\n",
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"9 1\n",
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"6 1\n",
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"5 1\n",
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"3 1\n",
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"1 1\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"print(\"Zbiór wartości:\", dane.unique())\n",
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"print(\"Zliczanie\", dane.value_counts())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Metoda ```value_counts``` zwraca nam szereg danych, który możemy wykorzystać do dalszych badań. Na przyklad, żeby wyświetlić 5 najczęściej występujących wartości, możemy napisać:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"15 4\n",
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"13 3\n",
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"4 3\n",
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"12 2\n",
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"7 2\n",
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"dtype: int64\n"
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]
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}
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],
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"source": [
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"print(dane.value_counts().head())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"Żeby uzyskać wszystkie podstawowe statystyki, możmey wywołać metodę ```describe```:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"count 25.000000\n",
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"mean 9.720000\n",
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"std 5.556678\n",
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"min 1.000000\n",
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"25% 4.000000\n",
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"50% 12.000000\n",
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"75% 15.000000\n",
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"max 19.000000\n",
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"dtype: float64\n"
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]
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}
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],
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"source": [
|
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"print(dane.describe())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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||
"slide_type": "slide"
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||
}
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||
},
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"source": [
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"A żeby wyświetlić je w postaci wykresu:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.axes._subplots.AxesSubplot at 0x7f28547b2f98>"
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]
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},
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"execution_count": 36,
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||
"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEACAYAAAB4ayemAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f285474be10>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"dane.hist()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "skip"
|
||
}
|
||
},
|
||
"source": [
|
||
"(Dane zostały wygenerowane w sposób losowy, stąd ich analiza jak na razie jest pozbawiona sensu.)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Indeksowanie, czyli dostęp do danych"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "skip"
|
||
}
|
||
},
|
||
"source": [
|
||
"Stwórzmy szereg danych, którego indeks będzie składać się z wielkich liter alfabetu:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 46,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"A 11\n",
|
||
"B 13\n",
|
||
"C 6\n",
|
||
"D 9\n",
|
||
"E 18\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import string\n",
|
||
"litery = list(string.ascii_uppercase)\n",
|
||
"dane3 = pd.Series(losowe, index=litery)\n",
|
||
"print(dane3.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Najprostszym sposobem dostępu do danych jest przez indeks:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 52,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"18\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(dane3['E'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Szeregi udostępniają wiele więcej. Jeżeli chcemy zobaczyć przykłady o kluczach *P*, *Y*, *T*, to możemy podać listę indeksów jako argument:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 53,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"P 12\n",
|
||
"Y 9\n",
|
||
"T 7\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(dane3[['P', 'Y', 'T']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Możemy również podać zakres danych:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 57,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"B 13\n",
|
||
"C 6\n",
|
||
"D 9\n",
|
||
"E 18\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(dane3['B':'E'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Jeżeli zmienimy indeks szeregu, to cay czas mamy możliwość pracy na indeskach liczbowych:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 58,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"C 6\n",
|
||
"D 9\n",
|
||
"E 18\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(dane3[2:5])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Mapowanie"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Szeregi pozwalają zmieniać dane, które przechowują. Pojedyńcze wartości mozemy zmieniać przy pomocy odwołania się do konkretnego elementu:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"777\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dane3[2] = 777\n",
|
||
"print(dane3[2])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Jeżeli chcemy zmienić cały szereg przy pomocy funkcji, możemy wykorzystać metodę ```map```:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"A 1331\n",
|
||
"B 2197\n",
|
||
"C 469097433\n",
|
||
"D 729\n",
|
||
"E 5832\n",
|
||
"F 1000\n",
|
||
"G 343\n",
|
||
"H 512\n",
|
||
"I 125\n",
|
||
"J 8\n",
|
||
"K 3375\n",
|
||
"L 4096\n",
|
||
"M 216\n",
|
||
"N 5832\n",
|
||
"O 343\n",
|
||
"P 1728\n",
|
||
"Q 4913\n",
|
||
"R 1728\n",
|
||
"S 2197\n",
|
||
"T 343\n",
|
||
"U 4913\n",
|
||
"V 3375\n",
|
||
"W 2744\n",
|
||
"X 1331\n",
|
||
"Y 729\n",
|
||
"Z 4096\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def cube(x):\n",
|
||
" return x ** 3\n",
|
||
"print(dane3.map(cube))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "skip"
|
||
}
|
||
},
|
||
"source": [
|
||
"*Uwaga:* w Pythonie istnieją funkcje lambda, które można tu wykorzystać."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Ramki danych"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Ramka danych jest odpowiednikiem tabeli znanej z R lub sqla. Patrząc z innego punktu widzenia, jest lista szeregóœ danych, które są połącząne z sobą wspólnym indeksem. Stwórzmy ramkę danych składających się z małych i wielkich liter:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 68,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[('a', 'A'), ('b', 'B'), ('c', 'C'), ('d', 'D'), ('e', 'E'), ('f', 'F'), ('g', 'G'), ('h', 'H'), ('i', 'I'), ('j', 'J'), ('k', 'K'), ('l', 'L'), ('m', 'M'), ('n', 'N'), ('o', 'O'), ('p', 'P'), ('q', 'Q'), ('r', 'R'), ('s', 'S'), ('t', 'T'), ('u', 'U'), ('v', 'V'), ('w', 'W'), ('x', 'X'), ('y', 'Y'), ('z', 'Z')]\n",
|
||
" 0 1\n",
|
||
"0 a A\n",
|
||
"1 b B\n",
|
||
"2 c C\n",
|
||
"3 d D\n",
|
||
"4 e E\n",
|
||
"5 f F\n",
|
||
"6 g G\n",
|
||
"7 h H\n",
|
||
"8 i I\n",
|
||
"9 j J\n",
|
||
"10 k K\n",
|
||
"11 l L\n",
|
||
"12 m M\n",
|
||
"13 n N\n",
|
||
"14 o O\n",
|
||
"15 p P\n",
|
||
"16 q Q\n",
|
||
"17 r R\n",
|
||
"18 s S\n",
|
||
"19 t T\n",
|
||
"20 u U\n",
|
||
"21 v V\n",
|
||
"22 w W\n",
|
||
"23 x X\n",
|
||
"24 y Y\n",
|
||
"25 z Z\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"wielkie = list(string.ascii_uppercase)\n",
|
||
"male = list(string.ascii_lowercase)\n",
|
||
"surowe = list(zip(male, wielkie))\n",
|
||
"print(surowe)\n",
|
||
"\n",
|
||
"dane = pd.DataFrame(surowe)\n",
|
||
"print(dane)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Jak widzimy, ramkę danych tworzymy podając listę przykładów. W powyższej ramce mamy dwie kolumny nazwane *0* i *1*. Zmieńmy te nazwy na bardziej czytelne:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 70,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" małe wielkie\n",
|
||
"0 a A\n",
|
||
"1 b B\n",
|
||
"2 c C\n",
|
||
"3 d D\n",
|
||
"4 e E\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"dane.columns = [\"małe\", \"wielkie\"]\n",
|
||
"print(dane.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Obsługa ramki danych nie różni się za bardzo od obsługi szeregu, np. działaja metody head i tail, jak i inne:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"małe z\n",
|
||
"wielkie Z\n",
|
||
"dtype: object\n",
|
||
" małe wielkie\n",
|
||
"count 26 26\n",
|
||
"unique 26 26\n",
|
||
"top j M\n",
|
||
"freq 1 1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(dane.max())\n",
|
||
"print(dane.describe())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"source": [
|
||
"Dodajmy trzecią kolumnę składającą się z losowych liczb:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 75,
|
||
"metadata": {
|
||
"collapsed": true,
|
||
"slideshow": {
|
||
"slide_type": "fragment"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"dane['losowe'] = np.random.randint(1, 20, 26)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"df = pd.read_csv(\"./titanic_train.tsv\", sep='\\t', index_col='PassengerId')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
|
||
" 'Fare', 'Cabin', 'Embarked'],\n",
|
||
" dtype='object')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(df.columns)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"Int64Index: 891 entries, 1 to 891\n",
|
||
"Data columns (total 11 columns):\n",
|
||
"Survived 891 non-null int64\n",
|
||
"Pclass 891 non-null int64\n",
|
||
"Name 891 non-null object\n",
|
||
"Sex 891 non-null object\n",
|
||
"Age 714 non-null float64\n",
|
||
"SibSp 891 non-null int64\n",
|
||
"Parch 891 non-null int64\n",
|
||
"Ticket 891 non-null object\n",
|
||
"Fare 891 non-null float64\n",
|
||
"Cabin 204 non-null object\n",
|
||
"Embarked 889 non-null object\n",
|
||
"dtypes: float64(2), int64(4), object(5)\n",
|
||
"memory usage: 83.5+ KB\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"df.info()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"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>Survived</th>\n",
|
||
" <th>Pclass</th>\n",
|
||
" <th>Name</th>\n",
|
||
" <th>Sex</th>\n",
|
||
" <th>Age</th>\n",
|
||
" <th>SibSp</th>\n",
|
||
" <th>Parch</th>\n",
|
||
" <th>Ticket</th>\n",
|
||
" <th>Fare</th>\n",
|
||
" <th>Cabin</th>\n",
|
||
" <th>Embarked</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>PassengerId</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></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>1</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Braund\\t Mr. Owen Harris</td>\n",
|
||
" <td>male</td>\n",
|
||
" <td>22.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>A/5 21171</td>\n",
|
||
" <td>7.2500</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Cumings\\t Mrs. John Bradley (Florence Briggs T...</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>38.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>PC 17599</td>\n",
|
||
" <td>71.2833</td>\n",
|
||
" <td>C85</td>\n",
|
||
" <td>C</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Heikkinen\\t Miss. Laina</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>26.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>STON/O2. 3101282</td>\n",
|
||
" <td>7.9250</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Futrelle\\t Mrs. Jacques Heath (Lily May Peel)</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>35.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>113803</td>\n",
|
||
" <td>53.1000</td>\n",
|
||
" <td>C123</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Allen\\t Mr. William Henry</td>\n",
|
||
" <td>male</td>\n",
|
||
" <td>35.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>373450</td>\n",
|
||
" <td>8.0500</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Survived Pclass \\\n",
|
||
"PassengerId \n",
|
||
"1 0 3 \n",
|
||
"2 1 1 \n",
|
||
"3 1 3 \n",
|
||
"4 1 1 \n",
|
||
"5 0 3 \n",
|
||
"\n",
|
||
" Name Sex Age \\\n",
|
||
"PassengerId \n",
|
||
"1 Braund\\t Mr. Owen Harris male 22.0 \n",
|
||
"2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n",
|
||
"3 Heikkinen\\t Miss. Laina female 26.0 \n",
|
||
"4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n",
|
||
"5 Allen\\t Mr. William Henry male 35.0 \n",
|
||
"\n",
|
||
" SibSp Parch Ticket Fare Cabin Embarked \n",
|
||
"PassengerId \n",
|
||
"1 1 0 A/5 21171 7.2500 NaN S \n",
|
||
"2 1 0 PC 17599 71.2833 C85 C \n",
|
||
"3 0 0 STON/O2. 3101282 7.9250 NaN S \n",
|
||
"4 1 0 113803 53.1000 C123 S \n",
|
||
"5 0 0 373450 8.0500 NaN S "
|
||
]
|
||
},
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"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>Survived</th>\n",
|
||
" <th>Pclass</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>PassengerId</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Survived Pclass\n",
|
||
"PassengerId \n",
|
||
"1 0 3\n",
|
||
"2 1 1\n",
|
||
"3 1 3\n",
|
||
"4 1 1\n",
|
||
"5 0 3"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[['Survived', 'Pclass']].head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"(891, 11)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(df.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Survived 1\n",
|
||
"Pclass 1\n",
|
||
"Name Graham\\t Miss. Margaret Edith\n",
|
||
"Sex female\n",
|
||
"Age 19\n",
|
||
"SibSp 0\n",
|
||
"Parch 0\n",
|
||
"Ticket 112053\n",
|
||
"Fare 30\n",
|
||
"Cabin B42\n",
|
||
"Embarked S\n",
|
||
"Name: 888, dtype: object"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.loc[888]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"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>Survived</th>\n",
|
||
" <th>Pclass</th>\n",
|
||
" <th>Name</th>\n",
|
||
" <th>Sex</th>\n",
|
||
" <th>Age</th>\n",
|
||
" <th>SibSp</th>\n",
|
||
" <th>Parch</th>\n",
|
||
" <th>Ticket</th>\n",
|
||
" <th>Fare</th>\n",
|
||
" <th>Cabin</th>\n",
|
||
" <th>Embarked</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>PassengerId</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></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>888</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Graham\\t Miss. Margaret Edith</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>19.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>112053</td>\n",
|
||
" <td>30.00</td>\n",
|
||
" <td>B42</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>889</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Johnston\\t Miss. Catherine Helen \"Carrie\"</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>W./C. 6607</td>\n",
|
||
" <td>23.45</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>890</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Behr\\t Mr. Karl Howell</td>\n",
|
||
" <td>male</td>\n",
|
||
" <td>26.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>111369</td>\n",
|
||
" <td>30.00</td>\n",
|
||
" <td>C148</td>\n",
|
||
" <td>C</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>891</th>\n",
|
||
" <td>0</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Dooley\\t Mr. Patrick</td>\n",
|
||
" <td>male</td>\n",
|
||
" <td>32.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>370376</td>\n",
|
||
" <td>7.75</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>Q</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Survived Pclass Name \\\n",
|
||
"PassengerId \n",
|
||
"888 1 1 Graham\\t Miss. Margaret Edith \n",
|
||
"889 0 3 Johnston\\t Miss. Catherine Helen \"Carrie\" \n",
|
||
"890 1 1 Behr\\t Mr. Karl Howell \n",
|
||
"891 0 3 Dooley\\t Mr. Patrick \n",
|
||
"\n",
|
||
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
|
||
"PassengerId \n",
|
||
"888 female 19.0 0 0 112053 30.00 B42 S \n",
|
||
"889 female NaN 1 2 W./C. 6607 23.45 NaN S \n",
|
||
"890 male 26.0 0 0 111369 30.00 C148 C \n",
|
||
"891 male 32.0 0 0 370376 7.75 NaN Q "
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.loc[888:892]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"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>Survived</th>\n",
|
||
" <th>Pclass</th>\n",
|
||
" <th>Name</th>\n",
|
||
" <th>Sex</th>\n",
|
||
" <th>Age</th>\n",
|
||
" <th>SibSp</th>\n",
|
||
" <th>Parch</th>\n",
|
||
" <th>Ticket</th>\n",
|
||
" <th>Fare</th>\n",
|
||
" <th>Cabin</th>\n",
|
||
" <th>Embarked</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>PassengerId</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></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>2</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Cumings\\t Mrs. John Bradley (Florence Briggs T...</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>38.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>PC 17599</td>\n",
|
||
" <td>71.2833</td>\n",
|
||
" <td>C85</td>\n",
|
||
" <td>C</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Heikkinen\\t Miss. Laina</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>26.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>STON/O2. 3101282</td>\n",
|
||
" <td>7.9250</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Futrelle\\t Mrs. Jacques Heath (Lily May Peel)</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>35.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>113803</td>\n",
|
||
" <td>53.1000</td>\n",
|
||
" <td>C123</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Johnson\\t Mrs. Oscar W (Elisabeth Vilhelmina B...</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>27.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>347742</td>\n",
|
||
" <td>11.1333</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>S</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>Nasser\\t Mrs. Nicholas (Adele Achem)</td>\n",
|
||
" <td>female</td>\n",
|
||
" <td>14.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>237736</td>\n",
|
||
" <td>30.0708</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>C</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Survived Pclass \\\n",
|
||
"PassengerId \n",
|
||
"2 1 1 \n",
|
||
"3 1 3 \n",
|
||
"4 1 1 \n",
|
||
"9 1 3 \n",
|
||
"10 1 2 \n",
|
||
"\n",
|
||
" Name Sex Age \\\n",
|
||
"PassengerId \n",
|
||
"2 Cumings\\t Mrs. John Bradley (Florence Briggs T... female 38.0 \n",
|
||
"3 Heikkinen\\t Miss. Laina female 26.0 \n",
|
||
"4 Futrelle\\t Mrs. Jacques Heath (Lily May Peel) female 35.0 \n",
|
||
"9 Johnson\\t Mrs. Oscar W (Elisabeth Vilhelmina B... female 27.0 \n",
|
||
"10 Nasser\\t Mrs. Nicholas (Adele Achem) female 14.0 \n",
|
||
"\n",
|
||
" SibSp Parch Ticket Fare Cabin Embarked \n",
|
||
"PassengerId \n",
|
||
"2 1 0 PC 17599 71.2833 C85 C \n",
|
||
"3 0 0 STON/O2. 3101282 7.9250 NaN S \n",
|
||
"4 1 0 113803 53.1000 C123 S \n",
|
||
"9 0 2 347742 11.1333 NaN S \n",
|
||
"10 1 0 237736 30.0708 NaN C "
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[df.Survived == 1].head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"PassengerId\n",
|
||
"1 2\n",
|
||
"2 2\n",
|
||
"3 1\n",
|
||
"4 2\n",
|
||
"5 1\n",
|
||
"Name: FSize, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[\"FSize\"] = df.SibSp + df.Parch + 1\n",
|
||
"df.FSize.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"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>Survived</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Sex</th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>female</th>\n",
|
||
" <td>0.742038</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>male</th>\n",
|
||
" <td>0.188908</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Survived\n",
|
||
"Sex \n",
|
||
"female 0.742038\n",
|
||
"male 0.188908"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[['Sex', 'Survived']].groupby('Sex').mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"\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>Survived</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>FSize</th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>0.303538</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Survived\n",
|
||
"FSize \n",
|
||
"1 0.303538"
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[['FSize', 'Survived']].loc[df['FSize'] == 1].groupby('FSize').mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.30353817504655495"
|
||
]
|
||
},
|
||
"execution_count": 35,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df[['FSize', 'Survived']].groupby('FSize').mean().loc[1]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"metadata": {
|
||
"slideshow": {
|
||
"slide_type": "slide"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Survived 0\n",
|
||
"Pclass 3\n",
|
||
"Name Braund\\t Mr. Owen Harris\n",
|
||
"Sex male\n",
|
||
"Age 22\n",
|
||
"SibSp 1\n",
|
||
"Parch 0\n",
|
||
"Ticket A/5 21171\n",
|
||
"Fare 7.25\n",
|
||
"Cabin NaN\n",
|
||
"Embarked S\n",
|
||
"FSize 2\n",
|
||
"Name: 1, dtype: object\n",
|
||
"Survived 1\n",
|
||
"Pclass 1\n",
|
||
"Name Cumings\\t Mrs. John Bradley (Florence Briggs T...\n",
|
||
"Sex female\n",
|
||
"Age 38\n",
|
||
"SibSp 1\n",
|
||
"Parch 0\n",
|
||
"Ticket PC 17599\n",
|
||
"Fare 71.2833\n",
|
||
"Cabin C85\n",
|
||
"Embarked C\n",
|
||
"FSize 2\n",
|
||
"Name: 2, dtype: object\n",
|
||
"Survived 1\n",
|
||
"Pclass 3\n",
|
||
"Name Heikkinen\\t Miss. Laina\n",
|
||
"Sex female\n",
|
||
"Age 26\n",
|
||
"SibSp 0\n",
|
||
"Parch 0\n",
|
||
"Ticket STON/O2. 3101282\n",
|
||
"Fare 7.925\n",
|
||
"Cabin NaN\n",
|
||
"Embarked S\n",
|
||
"FSize 1\n",
|
||
"Name: 3, dtype: object\n",
|
||
"Survived 1\n",
|
||
"Pclass 1\n",
|
||
"Name Futrelle\\t Mrs. Jacques Heath (Lily May Peel)\n",
|
||
"Sex female\n",
|
||
"Age 35\n",
|
||
"SibSp 1\n",
|
||
"Parch 0\n",
|
||
"Ticket 113803\n",
|
||
"Fare 53.1\n",
|
||
"Cabin C123\n",
|
||
"Embarked S\n",
|
||
"FSize 2\n",
|
||
"Name: 4, dtype: object\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for idx, row in df.iterrows():\n",
|
||
" if idx < 5:\n",
|
||
" print(row)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"celltoolbar": "Slideshow",
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|