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Plt scatter size

matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, *, edgecolors=None, plotnonfinite=False, data=None, **kwargs)[source]? A scatter plot of y vs. x with varying marker size and/or color. Parameters: x, yfloat or array-like, shape (n, )The data positions. sfloat or array-like, shape (n, ), optionalThe marker size in points**2. Default is rcParams['lines.markersize'] ** 2. carray-like or list of colors or color, optionalThe marker colors. Possible values: A scalar or sequence of n numbers to be mapped to colors using cmap and norm. A 2D array in which the rows are RGB or RGBA. A sequence of colors of length n. A single color format string. Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. If you want to specify the same RGB or RGBA value for all points, use a 2D array with a single row. Otherwise, value- matching will have precedence in case of a size matching with x and y. If you wish to specify a single color for all points prefer the color keyword argument. Defaults to None. In that case the marker color is determined by the value of color, facecolor or facecolors. In case those are not specified or None, the marker color is determined by the next color of the Axes' current "shape and fill" color cycle. This cycle defaults to rcParams["axes.prop_cycle"] (default: cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'])). markerMarkerStyle, default: rcParams["scatter.marker"] (default: 'o')The marker style. marker can be either an instance of the class or the text shorthand for a particular marker. See matplotlib.markers for more information about marker styles. cmapstr or Colormap, default: rcParams["image.cmap"] (default: 'viridis')A Colormap instance or registered colormap name. cmap is only used if c is an array of floats. normNormalize, default: NoneIf c is an array of floats, norm is used to scale the color data, c, in the range 0 to 1, in order to map into the colormap cmap. If None, use the default colors.Normalize. vmin, vmaxfloat, default: Nonevmin and vmax are used in conjunction with the default norm to map the color array c to the colormap cmap. If None, the respective min and max of the color array is used. It is deprecated to use vmin/vmax when norm is given. alphafloat, default: NoneThe alpha blending value, between 0 (transparent) and 1 (opaque). linewidthsfloat or array-like, default: rcParams["lines.linewidth"] (default: 1.5)The linewidth of the marker edges. Note: The default edgecolors is 'face'. You may want to change this as well. edgecolors{'face', 'none', None} or color or sequence of color, default: rcParams["scatter.edgecolors"] (default: 'face')The edge color of the marker. Possible values: 'face': The edge color will always be the same as the face color. 'none': No patch boundary will be drawn. A color or sequence of colors. For non-filled markers, edgecolors is ignored. Instead, the color is determined like with 'face', i.e. from c, colors, or facecolors. plotnonfinitebool, default: FalseWhether to plot points with nonfinite c (i.e. inf, -inf or nan). If True the points are drawn with the bad colormap color (see Colormap.set_bad). Returns: PathCollection Other Parameters: **kwargsCollection properties See also plotTo plot scatter plots when markers are identical in size and color. Notes The plot function will be faster for scatterplots where markers don't vary in size or color. Any or all of x, y, s, and c may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted. Fundamentally, scatter works with 1D arrays; x, y, s, and c may be input as N-D arrays, but within scatter they will be flattened. The exception is c, which will be flattened only if its size matches the size of x and y. Note In addition to the above described arguments, this function can take a data keyword argument. If such a data argument is given, the following arguments can also be string s, which is interpreted as data[s] (unless this raises an exception): x, y, s, linewidths, edgecolors, c, facecolor, facecolors, color. Objects passed as data must support item access (data[s]) and membership test (s in data). %matplotlib inline import pandas as pd import matplotlib.pyplot as plt import numpy as np # Set ipython's max row display pd.set_option('display.max_row', 1000) # Set iPython's max column width to 50 pd.set_option('display.max_columns', 50) df = pd.read_csv(' ) df.head() name year battle_number attacker_king defender_king attacker_1 attacker_2 attacker_3 attacker_4 defender_1 defender_2 defender_3 defender_4 attacker_outcome battle_type major_death major_capture attacker_size defender_size attacker_commander defender_commander summer location region note 0 Battle of the Golden Tooth 298 1 Joffrey/Tommen Baratheon Robb Stark Lannister NaN NaN NaN Tully NaN NaN NaN win pitched battle 1.0 0.0 15000.0 4000.0 Jaime Lannister Clement Piper, Vance 1.0 Golden Tooth The Westerlands NaN 1 Battle at the Mummer's Ford 298 2 Joffrey/Tommen Baratheon Robb Stark Lannister NaN NaN NaN Baratheon NaN NaN NaN win ambush 1.0 0.0 NaN 120.0 Gregor Clegane Beric Dondarrion 1.0 Mummer's Ford The Riverlands NaN 2 Battle of Riverrun 298 3 Joffrey/Tommen Baratheon Robb Stark Lannister NaN NaN NaN Tully NaN NaN NaN win pitched battle 0.0 1.0 15000.0 10000.0 Jaime Lannister, Andros Brax Edmure Tully, Tytos Blackwood 1.0 Riverrun The Riverlands NaN 3 Battle of the Green Fork 298 4 Robb Stark Joffrey/Tommen Baratheon Stark NaN NaN NaN Lannister NaN NaN NaN loss pitched battle 1.0 1.0 18000.0 20000.0 Roose Bolton, Wylis Manderly, Medger Cerwyn, H... Tywin Lannister, Gregor Clegane, Kevan Lannist... 1.0 Green Fork The Riverlands NaN 4 Battle of the Whispering Wood 298 5 Robb Stark Joffrey/Tommen Baratheon Stark Tully NaN NaN Lannister NaN NaN NaN win ambush 1.0 1.0 1875.0 6000.0 Robb Stark, Brynden Tully Jaime Lannister 1.0 Whispering Wood The Riverlands NaN # Create a figure plt.figure(figsize=(10,8)) # Create a scatterplot of, # attacker size in year 298 as the x axis plt.scatter(df['attacker_size'] [df['year'] == 298], # defender size in year 298 as the y axis df['defender_size'][df['year'] == 298], # the marker as marker='x', # the color color='b', # the alpha alpha=0.7, # with size s = 124, # labelled this label='Year 298') # attacker size in year 299 as the x axis plt.scatter(df['attacker_size'][df['year'] == 299], # defender size in year 299 as the y axis df['defender_size'][df['year'] == 299], # the marker as marker='o', # the color color='r', # the alpha alpha=0.7, # with size s = 124, # labelled this label='Year 299') # attacker size in year 300 as the x axis plt.scatter(df['attacker_size'][df['year'] == 300], # defender size in year 300 as the y axis df['defender_size'][df['year'] == 300], # the marker as marker='^', # the color color='g', # the alpha alpha=0.7, # with size s = 124, # labelled this label='Year 300') # Chart title plt.title('Battles Of The War Of The Five Kings') # y label plt.ylabel('Defender Size') # x label plt.xlabel('Attacker Size') # and a legend plt.legend(loc='upper right') # set the figure boundaries plt.xlim([min(df['attacker_size'])-1000, max(df['attacker_size'])+1000]) plt.ylim([min(df['defender_size'])-1000, max(df['defender_size'])+1000]) plt.show() # x and y given as array_like objects import plotly.express as px fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16]) fig.show() # x and y given as DataFrame columns import plotly.express as px df = px.data.iris() # iris is a pandas DataFrame fig = px.scatter(df, x="sepal_width", y="sepal_length") fig.show() Note that color and size data are added to hover information. You can add other columns to hover data with the hover_data argument of px.scatter. import plotly.express as px df = px.data.iris() fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", size='petal_length', hover_data=['petal_width']) fig.show() Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py. Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise. import plotly.express as px import numpy as np t = np.linspace(0, 2*np.pi, 100) fig = px.line(x=t, y=np.cos(t), labels={'x':'t', 'y':'cos(t)'}) fig.show() import plotly.express as px df = px.data.gapminder().query("continent == 'Oceania'") fig = px.line(df, x='year', y='lifeExp', color='country') fig.show() If Plotly Express does not provide a good starting point, it is possible to use the more generic go.Scatter class from plotly.graph_objects. Whereas plotly.express has two functions scatter and line, go.Scatter can be used both for plotting points (makers) or lines, depending on the value of mode. The different options of go.Scatter are documented in its reference page. Simple Scatter Plot? import plotly.graph_objects as go import numpy as np N = 1000 t = np.linspace(0, 10, 100) y = np.sin(t) fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers')) fig.show() import plotly.graph_objects as go # Create random data with numpy import numpy as np np.random.seed(1) N = 100 random_x = np.linspace(0, 1, N) random_y0 = np.random.randn(N) + 5 random_y1 = np.random.randn(N) random_y2 = np.random.randn(N) - 5 fig = go.Figure() # Add traces fig.add_trace(go.Scatter(x=random_x, y=random_y0, mode='markers', name='markers')) fig.add_trace(go.Scatter(x=random_x, y=random_y1, mode='lines+markers', name='lines+markers')) fig.add_trace(go.Scatter(x=random_x, y=random_y2, mode='lines', name='lines')) fig.show() import plotly.graph_objects as go fig = go.Figure(data=go.Scatter( x=[1, 2, 3, 4], y=[10, 11, 12, 13], mode='markers', marker=dict(size=[40, 60, 80, 100], color=[0, 1, 2, 3]) )) fig.show() import plotly.graph_objects as go import numpy as np t = np.linspace(0, 10, 100) fig = go.Figure() fig.add_trace(go.Scatter( x=t, y=np.sin(t), name='sin', mode='markers', marker_color='rgba(152, 0, 0, .8)' )) fig.add_trace(go.Scatter( x=t, y=np.cos(t), name='cos', marker_color='rgba(255, 182, 193, .9)' )) # Set options common to all traces with fig.update_traces fig.update_traces(mode='markers', marker_line_width=2, marker_size=10) fig.update_layout(title='Styled Scatter', yaxis_zeroline=False, xaxis_zeroline=False) fig.show() import plotly.graph_objects as go import pandas as pd data= pd.read_csv(" ) fig = go.Figure(data=go.Scatter(x=data['Postal'], y=data['Population'], mode='markers', marker_color=data['Population'], text=data['State'])) # hover text goes here fig.update_layout(title='Population of USA States') fig.show() import plotly.graph_objects as go import numpy as np fig = go.Figure(data=go.Scatter( y = np.random.randn(500), mode='markers', marker=dict( size=16, color=np.random.randn(500), #set color equal to a variable colorscale='Viridis', # one of plotly colorscales showscale=True ) )) fig.show() Now in Plotly you can implement WebGL with Scattergl() in place of Scatter() for increased speed, improved interactivity, and the ability to plot even more data! import plotly.graph_objects as go import numpy as np N = 100000 fig = go.Figure(data=go.Scattergl( x = np.random.randn(N), y = np.random.randn(N), mode='markers', marker=dict( color=np.random.randn(N), colorscale='Viridis', line_width=1 ) )) fig.show() import plotly.graph_objects as go import numpy as np N = 100000 r = np.random.uniform(0, 1, N) theta = np.random.uniform(0, 2*np.pi, N) fig = go.Figure(data=go.Scattergl( x = r * np.cos(theta), # non-uniform distribution y = r * np.sin(theta), # zoom to see more points at the center mode='markers', marker=dict( color=np.random.randn(N), colorscale='Viridis', line_width=1 ) )) fig.show() Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library. Learn about how to install Dash at . Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this: import plotly.graph_objects as go # or plotly.express as px fig = go.Figure() # or any Plotly Express function e.g. px.bar(...) # fig.add_trace( ... ) # fig.update_layout( ... ) import dash import dash_core_components as dcc import dash_html_components as html app = dash.Dash() app.layout = html.Div([ dcc.Graph(figure=fig) ]) app.run_server(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter

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