990 lines
111 KiB
Plaintext
990 lines
111 KiB
Plaintext
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{
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Feuille 2 - UE Projet CMI-L1\n",
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"Reading files with Pandas, ploting and fitting data"
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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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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"# pandas is a Python package providing fast, flexible, and expressive data \n",
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"# structures designed to make working with “relational” or “labeled” data \n",
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"# both easy and intuitive. "
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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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"## I) Files : reading typical format files\n",
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"we propose to work with some column data :\n",
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"* csv : column data delimited by \",\" or \";\" with headers\n",
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"* txt : column data delimited by spaces with headers\n",
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"* raw : column data delimited by spaces without headers\n",
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"* dat : column data formatted delimited by spaces\n",
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"* xlsx : excel data"
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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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"### a) CSV files"
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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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"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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"x;y;er\r",
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"\r\n",
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"1;1;0.2\r",
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"\r\n",
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"2;4;0.2\r",
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"\r\n",
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"3;8;0.3\r",
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"\r\n",
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"4;17;0.4\r",
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"\r\n",
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"5;24;0.2"
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]
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}
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],
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"source": [
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"!cat data.csv"
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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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"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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" x y er\n",
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"0 1 1 0.2\n",
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"1 2 4 0.2\n",
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"2 3 8 0.3\n",
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"3 4 17 0.4\n",
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"4 5 24 0.2\n"
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]
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}
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],
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"source": [
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"# read file\n",
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"d = pd.read_csv(\"data.csv\",delimiter=\";\")\n",
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"print(d)"
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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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"### b) Raw files with headers"
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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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"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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"x y er\r",
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"\r\n",
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"1 1 0.2\r",
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"\r\n",
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"2 4 0.2\r",
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"\r\n",
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"3 8 0.3\r",
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"\r\n"
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]
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}
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],
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"source": [
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"# text files\n",
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"!cat data.txt"
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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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"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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" x y er\n",
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"0 1 1 0.2\n",
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"1 2 4 0.2\n",
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"2 3 8 0.3\n"
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]
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}
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],
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"source": [
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"# read file\n",
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"dtext = pd.read_csv(\"data.txt\")\n",
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"print(dtext)"
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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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"### c) Raw files without headers"
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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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"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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"1 1 0.2\r",
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"\r\n",
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"2 4 0.2\r",
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"\r\n",
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"3 8 0.3\r",
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"\r\n"
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]
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}
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],
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"source": [
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"# raw values\n",
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"!cat data.raw"
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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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"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 1 2\n",
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"0 1 1 0.2\n",
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"1 2 4 0.2\n",
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"2 3 8 0.3\n"
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]
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}
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],
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"source": [
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"# read raw file\n",
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"draw = pd.read_csv(\"data.raw\",delimiter=\" \", header = None)\n",
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"print(draw)"
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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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"### d) Formatted file"
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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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"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 1\n",
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"0 0.0 0.894520\n",
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"1 100.0 0.849616\n",
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"2 200.0 0.806313\n",
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"3 300.0 0.763861\n",
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"4 400.0 0.722299\n",
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"5 500.0 0.681680\n",
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"6 600.0 0.642211\n",
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"7 700.0 0.603411\n",
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"8 800.0 0.565199\n",
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"9 900.0 0.528873\n",
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"10 1000.0 0.492817\n"
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]
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}
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],
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"source": [
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"# read raw file\n",
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"fwidths = [11,10]\n",
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"dexpe = pd.read_fwf(\"expe.dat\",delimiter=\" \",header = None, widths = fwidths)\n",
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"print(dexpe)"
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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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"### e) Excel data"
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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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"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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" data Unnamed: 1 Unnamed: 2\n",
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"0 x y er\n",
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"1 1 1 0.2\n",
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"2 2 4 0.2\n",
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"3 3 8 0.3\n",
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"4 4 17 0.4\n",
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"5 5 24 0.2\n"
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]
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}
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],
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"source": [
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"d = pd.read_excel(\"data.xlsx\")\n",
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"print(d)"
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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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"### More formats\n",
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"see references https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html"
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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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"outputs": [
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{
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"data": {
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"text/html": [
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"<img src=\"02_io_readwrite.svg\" width=\"800\" height=\"500\"/>"
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],
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 10,
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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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"source": [
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"# import image module\n",
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"from IPython.display import Image\n",
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" \n",
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"# get the image\n",
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"Image(url=\"02_io_readwrite.svg\", width=800, height=500)"
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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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"## II) Filling variables"
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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": 11,
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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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" x y er\n",
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"0 1 1 0.2\n",
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"1 2 4 0.2\n",
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"2 3 8 0.3\n",
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"3 4 17 0.4\n",
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"4 5 24 0.2\n"
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]
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}
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],
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"source": [
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"# read file\n",
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"d = pd.read_csv(\"data.csv\",delimiter=\";\")\n",
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"print(d)"
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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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"We can use **headers** or **column numbers** to fill the variables"
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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": 12,
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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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"0 1\n",
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"1 2\n",
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"2 3\n",
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"3 4\n",
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"4 5\n",
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"Name: x, dtype: int64 0 1\n",
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"1 4\n",
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"2 8\n",
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"3 17\n",
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"4 24\n",
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"Name: y, dtype: int64 0 0.2\n",
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"1 0.2\n",
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"2 0.3\n",
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"3 0.4\n",
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"4 0.2\n",
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"Name: er, dtype: float64\n"
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]
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}
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],
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"source": [
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"# taking values from headers\n",
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"x=d[\"x\"]\n",
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"y=d[\"y\"]\n",
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"e=d[\"er\"]\n",
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"print(x,y,e)"
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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": 13,
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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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"0 1\n",
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"1 2\n",
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"2 3\n",
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"3 4\n",
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"4 5\n",
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"Name: x, dtype: int64 0 1\n",
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"1 4\n",
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"2 8\n",
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"3 17\n",
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"4 24\n",
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"Name: y, dtype: int64 0 0.2\n",
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"1 0.2\n",
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"2 0.3\n",
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"3 0.4\n",
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"4 0.2\n",
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"Name: er, dtype: float64\n"
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]
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}
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],
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"source": [
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"# taking values from colon .iloc[row,col] or .loc[row,col]\n",
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"x=d.iloc[:,0]\n",
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"y=d.iloc[:,1]\n",
|
||
|
|
"e=d.iloc[:,2]\n",
|
||
|
|
"print(x,y,e)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## III) Plotting data"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" 0 1\n",
|
||
|
|
"0 0.0 0.894520\n",
|
||
|
|
"1 100.0 0.849616\n",
|
||
|
|
"2 200.0 0.806313\n",
|
||
|
|
"3 300.0 0.763861\n",
|
||
|
|
"4 400.0 0.722299\n",
|
||
|
|
"5 500.0 0.681680\n",
|
||
|
|
"6 600.0 0.642211\n",
|
||
|
|
"7 700.0 0.603411\n",
|
||
|
|
"8 800.0 0.565199\n",
|
||
|
|
"9 900.0 0.528873\n",
|
||
|
|
"10 1000.0 0.492817\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# read raw file\n",
|
||
|
|
"fwidths = [11,10]\n",
|
||
|
|
"dexpe = pd.read_fwf(\"expe.dat\",delimiter=\" \",header = None, widths = fwidths)\n",
|
||
|
|
"print(dexpe)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# get x and y\n",
|
||
|
|
"x=dexpe.iloc[:,0]\n",
|
||
|
|
"y=dexpe.iloc[:,1]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x7fb0838ea250>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot\n",
|
||
|
|
"fig = plt.figure(figsize=(6,6)) # s\n",
|
||
|
|
"ax = plt.subplot() \n",
|
||
|
|
"ax.plot(x,y,\"o\",label=\"Experimental data\")\n",
|
||
|
|
"ax.set_ylim([0,2])\n",
|
||
|
|
"ax.set_xlabel(\"t[s]\")\n",
|
||
|
|
"ax.set_ylabel(\"V[t]\")\n",
|
||
|
|
"ax.set_title(\"Temporal Evolution of Volume\")\n",
|
||
|
|
"ax.legend()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## IV) Fitting data : linear"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from scipy.optimize import curve_fit\n",
|
||
|
|
"import numpy as np"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def func(x, a, b):\n",
|
||
|
|
" return a * x + b"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(array([-4.01437909e-04, 8.87155318e-01]), array([[ 1.96723961e-11, -9.83619802e-09],\n",
|
||
|
|
" [-9.83619802e-09, 6.88533863e-06]]))\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"popt= curve_fit(func, x, y)\n",
|
||
|
|
"print(popt)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"[a,b]=popt[0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x7fb083cadf40>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
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|
"text/plain": [
|
||
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|
"<Figure size 432x432 with 1 Axes>"
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||
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|
]
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},
|
||
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"metadata": {
|
||
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"needs_background": "light"
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},
|
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"output_type": "display_data"
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||
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}
|
||
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],
|
||
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"source": [
|
||
|
|
"fig = plt.figure(figsize=(6,6)) # \n",
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||
|
|
"ax = plt.subplot() \n",
|
||
|
|
"ax.plot(x,y,\"o\",label=\"Experimental data\")\n",
|
||
|
|
"ax.plot(x,func(x,*popt[0]),label=\"Linear model\")\n",
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||
|
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"ax.set_ylim([0,2])\n",
|
||
|
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"ax.set_xlabel(\"t[s]\")\n",
|
||
|
|
"ax.set_ylabel(\"V[t]\")\n",
|
||
|
|
"ax.set_title(\"Temporal Evolution of Volume\")\n",
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||
|
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"ax.legend()"
|
||
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]
|
||
|
|
},
|
||
|
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{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## V) Fitting data : non linear"
|
||
|
|
]
|
||
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|
},
|
||
|
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{
|
||
|
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"cell_type": "code",
|
||
|
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"execution_count": 23,
|
||
|
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"metadata": {},
|
||
|
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"outputs": [
|
||
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{
|
||
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"data": {
|
||
|
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"text/html": [
|
||
|
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"<div>\n",
|
||
|
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
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|
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
|
||
|
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" vertical-align: top;\n",
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||
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" }\n",
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"\n",
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|
|
" .dataframe thead th {\n",
|
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" 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>0</th>\n",
|
||
|
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" <th>1</th>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>0.102</td>\n",
|
||
|
|
" <td>0.319</td>\n",
|
||
|
|
" <td>0.565</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>0.204</td>\n",
|
||
|
|
" <td>0.452</td>\n",
|
||
|
|
" <td>0.672</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>0.306</td>\n",
|
||
|
|
" <td>0.553</td>\n",
|
||
|
|
" <td>0.744</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>0.408</td>\n",
|
||
|
|
" <td>0.639</td>\n",
|
||
|
|
" <td>0.799</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>0.510</td>\n",
|
||
|
|
" <td>0.714</td>\n",
|
||
|
|
" <td>0.845</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" 0 1 2\n",
|
||
|
|
"0 0.102 0.319 0.565\n",
|
||
|
|
"1 0.204 0.452 0.672\n",
|
||
|
|
"2 0.306 0.553 0.744\n",
|
||
|
|
"3 0.408 0.639 0.799\n",
|
||
|
|
"4 0.510 0.714 0.845"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# read raw file\n",
|
||
|
|
"fwidths = [6,6,6]\n",
|
||
|
|
"dexpe = pd.read_fwf(\"power.dat\",delimiter=\" \",header = None, widths = fwidths)\n",
|
||
|
|
"dexpe.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# get x and y\n",
|
||
|
|
"x =dexpe.iloc[:,0]\n",
|
||
|
|
"y1=dexpe.iloc[:,1]\n",
|
||
|
|
"y2=dexpe.iloc[:,2]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x7fb083dbd130>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot\n",
|
||
|
|
"fig = plt.figure(figsize=(6,6)) # s\n",
|
||
|
|
"ax = plt.subplot() \n",
|
||
|
|
"ax.plot(x,y1,\"o\",label=\"Experimental data 1\")\n",
|
||
|
|
"ax.plot(x,y2,\"o\",label=\"Experimental data 2\")\n",
|
||
|
|
"ax.set_ylim([0,2])\n",
|
||
|
|
"ax.set_xlabel(\"t[s]\")\n",
|
||
|
|
"ax.set_ylabel(\"V[t]\")\n",
|
||
|
|
"ax.set_title(\"Temporal Evolution of Volume\")\n",
|
||
|
|
"ax.legend()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### log-log"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot\n",
|
||
|
|
"fig = plt.figure(figsize=(6,6)) # s\n",
|
||
|
|
"ax = plt.subplot() \n",
|
||
|
|
"ax.plot(x,y1,\"o\",label=\"Experimental data 1\")\n",
|
||
|
|
"ax.plot(x,y2,\"o\",label=\"Experimental data 2\")\n",
|
||
|
|
"ax.set_xlabel(\"t[s]\")\n",
|
||
|
|
"ax.set_ylabel(\"V[t]\")\n",
|
||
|
|
"ax.set_title(\"Temporal Evolution of Volume\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.loglog()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"That's because\n",
|
||
|
|
"$$ y = x^a + b $$\n",
|
||
|
|
"gives\n",
|
||
|
|
"$$\\log(y) = a \\log(x) + \\log(b)$$\n",
|
||
|
|
"a linear function en log-log scale"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def func(x, a, b):\n",
|
||
|
|
" return a * x + b"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"popt= curve_fit(func, np.log(x), np.log(y1))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"0.500\n",
|
||
|
|
"-0.000\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"popt[0]\n",
|
||
|
|
"a=popt[0][0]\n",
|
||
|
|
"b=popt[0][1]\n",
|
||
|
|
"print(\"%.3f\" % a)\n",
|
||
|
|
"print(\"%.3f\" % b)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# plot\n",
|
||
|
|
"fig = plt.figure(figsize=(6,6)) # s\n",
|
||
|
|
"ax = plt.subplot() \n",
|
||
|
|
"ax.plot(x,y1,\"o\",label=\"Experimental data 1\")\n",
|
||
|
|
"ax.plot(x,x**a+b,\"-\",label=\"Model 1\")\n",
|
||
|
|
"ax.set_xlabel(\"t[s]\")\n",
|
||
|
|
"ax.set_ylabel(\"V[t]\")\n",
|
||
|
|
"ax.set_title(\"Temporal Evolution of Volume\")\n",
|
||
|
|
"ax.legend()\n",
|
||
|
|
"ax.loglog()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### VI ) Error bars"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" x y er\n",
|
||
|
|
"0 1 1 0.2\n",
|
||
|
|
"1 2 4 0.2\n",
|
||
|
|
"2 3 8 0.3\n",
|
||
|
|
"3 4 17 0.4\n",
|
||
|
|
"4 5 24 0.2\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# read file\n",
|
||
|
|
"d = pd.read_csv(\"data.csv\",delimiter=\";\")\n",
|
||
|
|
"print(d.head())\n",
|
||
|
|
"# taking values from headers\n",
|
||
|
|
"x=d[\"x\"]\n",
|
||
|
|
"y=d[\"y\"]\n",
|
||
|
|
"e=d[\"er\"]\n",
|
||
|
|
"#print(x,y,e)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.legend.Legend at 0x7fb0828c64f0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x432 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig = plt.figure(figsize=(6,6)) # s\n",
|
||
|
|
"ax = plt.subplot() \n",
|
||
|
|
"ax.errorbar(x,0.1*y,e*2, marker='.', linestyle=\"none\", label=\"data\")\n",
|
||
|
|
"ax.set_title(\"with error bars\")\n",
|
||
|
|
"ax.legend()"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"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": "3.9.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|