探索的データ解析における正しい可視化手法の選び方と描き方 - Qiita
データ分析における関数の使い方については様々な記事が上がっています。関数を知らなかったり使い方が分からないときは調べればだいたい答えが見つかります。 一方で、実際に分析を始めようとすると、たとえ関数の使い方がわかっていても、データをどのような切り口から何を分析・可視化していけば良いのか困ってしまうことがよくあります。 この記事では、あんちべさんが書いたデータ解析の実務プロセス入門という本をベースに、どのようなデータから何を見たいときにどのような可視化手法を使えばよいのかを、具体例を交えながら整理していきます。 探索的データ解析とは データ解析のアプローチは、大きく分けて仮説をデータで検証する「仮説検証型」とデータから仮説を生み出す「探索型」に分けられます。 実際にデータ解析を行うときは、仮説検証型と探索型を行き来しつつ知見を見出していきます。 データ解析には検証すべき仮説を設定することが必
Equidistant Cylindrical Projection — Basemap Matplotlib Toolkit 1.2.1 documentation
Equidistant Cylindrical Projection¶ The simplest projection, just displays the world in latitude/longitude coordinates. from mpl_toolkits.basemap import Basemap import numpy as np import matplotlib.pyplot as plt # llcrnrlat,llcrnrlon,urcrnrlat,urcrnrlon # are the lat/lon values of the lower left and upper right corners # of the map. # resolution = 'c' means use crude resolution coastlines. m = Bas
Text rendering With LaTeX — Matplotlib 2.0.2 documentation
Text rendering With LaTeX¶ Matplotlib has the option to use LaTeX to manage all text layout. This option is available with the following backends: Agg PS PDF The LaTeX option is activated by setting text.usetex : True in your rc settings. Text handling with matplotlib’s LaTeX support is slower than matplotlib’s very capable mathtext, but is more flexible, since different LaTeX packages (font packa
[Python]Matplotlibで複数のグラフを描画する方法 - Qiita
import matplotlib.pyplot as plt import numpy as np t = np.linspace(-np.pi, np.pi, 1000) x1 = np.sin(2*t) x2 = np.cos(2*t) fig, (axL, axR) = plt.subplots(ncols=2, figsize=(10,4)) axL.plot(t, x1, linewidth=2) axL.set_title('sin') axL.set_xlabel('t') axL.set_ylabel('x') axL.set_xlim(-np.pi, np.pi) axL.grid(True) axR.plot(t, x2, linewidth=2) axR.set_title('cos') axR.set_xlabel('t') axR.set_ylabel('x')
![[Python]Matplotlibで複数のグラフを描画する方法 - Qiita](https://cdn-ak-scissors.b.st-hatena.com/image/square/37399bd72ce1455ccca90231befcdd6d8a9344ca/height=288;version=1;width=512/https%3A%2F%2Fqiita-user-contents.imgix.net%2Fhttps%253A%252F%252Fqiita-user-contents.imgix.net%252Fhttps%25253A%25252F%25252Fcdn.qiita.com%25252Fassets%25252Fpublic%25252Farticle-ogp-background-afbab5eb44e0b055cce1258705637a91.png%253Fixlib%253Drb-4.0.0%2526w%253D1200%2526blend64%253DaHR0cHM6Ly9xaWl0YS11c2VyLXByb2ZpbGUtaW1hZ2VzLmltZ2l4Lm5ldC9odHRwcyUzQSUyRiUyRnFpaXRhLWltYWdlLXN0b3JlLnMzLmFtYXpvbmF3cy5jb20lMkYwJTJGMTAwNTIzJTJGcHJvZmlsZS1pbWFnZXMlMkYxNDczNzA4MTIwP2l4bGliPXJiLTQuMC4wJmFyPTElM0ExJmZpdD1jcm9wJm1hc2s9ZWxsaXBzZSZmbT1wbmczMiZzPTlmNjVlN2Y3NTE0MGQwZWNmYjAwNzhkMmM3ZDJmMTZj%2526blend-x%253D120%2526blend-y%253D467%2526blend-w%253D82%2526blend-h%253D82%2526blend-mode%253Dnormal%2526s%253D1c718de2b30c00590130a4e7c1781925%3Fixlib%3Drb-4.0.0%26w%3D1200%26fm%3Djpg%26mark64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTk2MCZoPTMyNCZ0eHQ9JTVCUHl0aG9uJTVETWF0cGxvdGxpYiVFMyU4MSVBNyVFOCVBNCU4NyVFNiU5NSVCMCVFMyU4MSVBRSVFMyU4MiVCMCVFMyU4MyVBOSVFMyU4MyU5NSVFMyU4MiU5MiVFNiU4RiU4RiVFNyU5NCVCQiVFMyU4MSU5OSVFMyU4MiU4QiVFNiU5NiVCOSVFNiVCMyU5NSZ0eHQtYWxpZ249bGVmdCUyQ3RvcCZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTU2JnR4dC1wYWQ9MCZzPTMwZDdkY2IzMDJhMjg1ZWVhYmZkNTM1ZjI1Y2UyYTk2%26mark-x%3D120%26mark-y%3D112%26blend64%3DaHR0cHM6Ly9xaWl0YS11c2VyLWNvbnRlbnRzLmltZ2l4Lm5ldC9-dGV4dD9peGxpYj1yYi00LjAuMCZ3PTgzOCZoPTU4JnR4dD0lNDBzdXBlcnNhaWFrdWppbiZ0eHQtY29sb3I9JTIzMUUyMTIxJnR4dC1mb250PUhpcmFnaW5vJTIwU2FucyUyMFc2JnR4dC1zaXplPTM2JnR4dC1wYWQ9MCZzPTllZGIzOTQ1YThjNDUxMjIwMWZhYTU3YjM2MTYwODI5%26blend-x%3D242%26blend-y%3D480%26blend-w%3D838%26blend-h%3D46%26blend-fit%3Dcrop%26blend-crop%3Dleft%252Cbottom%26blend-mode%3Dnormal%26s%3D1862c4267cd7b4b24d06279354c3df75)
Multiple subplots — Matplotlib 3.10.1 documentation
Multiple subplots# Simple demo with multiple subplots. For more options, see Create multiple subplots using plt.subplots. import matplotlib.pyplot as plt import numpy as np # Create some fake data. x1 = np.linspace(0.0, 5.0) y1 = np.cos(2 * np.pi * x1) * np.exp(-x1) x2 = np.linspace(0.0, 2.0) y2 = np.cos(2 * np.pi * x2)
images_contours_and_fields example code: pcolormesh_levels.py — Matplotlib 2.0.2 documentation
""" Shows how to combine Normalization and Colormap instances to draw "levels" in pcolor, pcolormesh and imshow type plots in a similar way to the levels keyword argument to contour/contourf. """ import matplotlib.pyplot as plt from matplotlib.colors import BoundaryNorm from matplotlib.ticker import MaxNLocator import numpy as np # make these smaller to increase the resolution dx, dy = 0.05, 0.05
mplot3d tutorial — Matplotlib 2.0.2 documentation
You are reading an old version of the documentation (v2.0.2). For the latest version see https://matplotlib.org/stable/
matplotlib.pyplot.legend — Matplotlib 2.2.2 documentation
matplotlib.pyplot.legend# matplotlib.pyplot.legend(*args, **kwargs)[source]# Place a legend on the Axes. Call signatures: legend() legend(handles, labels) legend(handles=handles) legend(labels) The call signatures correspond to the following different ways to use this method: 1. Automatic detection of elements to be shown in the legend The elements to be added to the legend are automatically deter
matplotlib.figure.Figure — Matplotlib 3.3.4 documentation
matplotlib.figure.Figure¶ class matplotlib.figure.Figure(figsize=None, dpi=None, facecolor=None, edgecolor=None, linewidth=0.0, frameon=None, subplotpars=None, tight_layout=None, constrained_layout=None)[source]¶ Bases: matplotlib.artist.Artist The top level container for all the plot elements. The Figure instance supports callbacks through a callbacks attribute which is a CallbackRegistry instanc
Matplotlib: Python plotting — Matplotlib 2.0.0 documentation
Matplotlib: Visualization with Python Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Create publication quality plots. Make interactive figures that can zoom, pan, update. Customize visual style and layout. Export to many file formats. Embed in JupyterLab and Graphical User I
matplotlibだけで画像からマウスで指定した領域を抜き出す - Qiita
import numpy as np import matplotlib.pyplot as plt # 押した時 def Press(event): global x1,y1,DragFlag # 値がNoneなら終了 if (event.xdata is None) or (event.ydata is None): return # 丸める cx = int(round(event.xdata)) cy = int(round(event.ydata)) x1 = cx y1 = cy # フラグをたてる DragFlag = True # ドラッグした時 def Drag(event): global x1,y1,x2,y2,DragFlag # ドラッグしていなければ終了 if DragFlag == False: return # 値がNoneなら終了 if (event.xd
3分でmatplotlibを日本語対応させる - Qiita
pythonのmatplotlibで日本語を扱う Qiitaにもいくつかおんなじような記事あるんですが、なんか分かりにくかったので自分の為にまとめておく 状況としては、macOS Sierra でmatplotlibのデフォルトのフォントが日本語対応しておらず、▫︎▫︎▫︎に(豆腐というらしい) とりあえず、グラフに日本語が表示されてほしい！ 手順 http://ipafont.ipa.go.jp/old/ipafont/download.html に行って、TTFファイルの4書体パックをダウンロード |font |name |family| ---|---|---|---| |ipagp.ttf | IPAPGothic| [sans-serif] |ipamp.ttf | IPAPMincho | [sans-serif] |ipam.ttf | IPAMincho | [sans-s
matplotlibを使って時系列グラフを書いてみる - Qiita
Python is not installed as a framework. The Mac OS X backend will not be able to function correctly if Python is not installed as a framework. See the Python documentation for more information on installing Python as a framework on Mac OS X. Please either reinstall Python as a framework, or try one of the other backends. import sys from datetime import datetime import matplotlib.pyplot as plt impo
Horizontal bar chart — Matplotlib 3.10.3 documentation
Horizontal bar chart# This example showcases a simple horizontal bar chart. import matplotlib.pyplot as plt import numpy as np # Fixing random state for reproducibility np.random.seed(19680801) fig, ax = plt.subplots() # Example data people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim') y_pos = np.arange(len(people)) performance = 3 + 10 * np.random.rand(len(people)) error = np.random.rand(len(people))
Tight Layout guide — Matplotlib 3.5.0 documentation
Tight Layout guide¶ How to use tight-layout to fit plots within your figure cleanly. tight_layout automatically adjusts subplot params so that the subplot(s) fits in to the figure area. This is an experimental feature and may not work for some cases. It only checks the extents of ticklabels, axis labels, and titles. An alternative to tight_layout is constrained_layout. Simple Example¶ In matplotli
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matplotlibで簡単にアニメーションをつくる（mp4, gif) - Qiita
matplotlib ログスケール表示とグリッド表示 - Qiita
shapes_and_collections example code: scatter_demo.py — Matplotlib 2.0.2 documentation
pylab_examples example code: dashpointlabel.py — Matplotlib 2.0.2 documentation