Matplotlib legend multiple columns

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Matplotlib legend multiple columns

In [1273]: from pandas.tools.plotting import scatter_matrix In [1274]: df = DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd']) In [1275]: scatter_matrix(df, alpha=0.2, figsize=(8, 8), diagonal='kde') Out[1275]: array([[Axes(0.125,0.7;0.19375x0.2), Axes(0.31875,0.7;0.19375x0.2), Axes(0.5125,0.7;0.19375x0.2), Axes(0.70625,0.7;0.19375x0.2)], [Axes(0.125,0.5;0.19375x0.2), Axes(0.31875,0.5;0.19375x0.2), Axes(0.5125,0.5;0.19375x0.2), Axes(0.70625,0.5;0.19375x0.2)], [Axes(0.125,0.3;0.19375x0.2), Axes(0.31875,0.3;0.19375x0.2), Axes(0.5125,0.3;0.19375x0.2), Axes(0.70625,0.3;0.19375x0.2)], [Axes(0.125,0.1;0.19375x0.2), Axes(0.31875,0.1;0.19375x0.2), Axes(0.5125,0.1;0.19375x0.2), Axes(0.70625,0.1;0.19375x0.2)]], dtype=object) Plot legends give meaning to a visualization, assigning meaning to the various plot elements. I have previously seen to create a simple legend; here we take a look at the customization of the placement and aesthetics of the legend in Matplotlib. The simplest caption can be created with the plt.legend() command that automatically creates a caption for any parcel items labeled: import matplotlib.pyplot as plt.style.use('classic') %matplotlib inline import numpy as np x = np.linspace(0, 10, 1000) fig, ax = plt.subplots() ax.plot(x, np.sin(x), '-b', label='Sine') ax.plot(x, np.cos(x), '-r', label='Cosine') ax.axis('equal') foot = ax.legend(); But there are many ways in which we might want to personalize such a legend. For example, we can specify the location and disable the frame: ax.legend(place='top left', frameon=False) fig We can use the ncol command to specify the number of columns in the caption: ax.legend(frameon=False, place='lower center', ncol=2) fig We can use a rounded box (fancybox) or we can add a shadow, we can change the transparency (alpha value) of the frame or we can change the lining around the text: ax.legend(fancybox=True, frasalpha=1, shadow=True, borderpad=1) fig For more information about available caption options, see doctrinal plt.legend. After I've already seen it, the caption includes all items tagged by default. If this is not what you want, we can adjust the items and labels that appear in the caption using objects returned by the graphic commands. The plt.plot() command is able to create multiple lines at once and returns a list of instances of created lines. Switching any of these to plt.legend() will tell you which to identify along with the labels that we would like to specify: y = np.sin(x[:, np.newaxis] + np.pi * np.arange(0, 2, 0.5)) lines = plt.plot(x, y) # lines is a list of plt. Line2D instances plt.legend(lines[:2], ['first', 'second']); In general, I find in practice that it is clearer to use the first method by applying labels to the parcel elements that you want to display on the caption: plt.plot(x, y[:, 0], label='first') plt.plot(x, y[:, 1], label='second') plt.plot(x, y[:, 2:]) plt. (legend frameon=); Notice that by default, the caption ignores all items without a set of label attributes. Sometimes the legend is the default is not sufficient for the given view. For example, you might use the dot size to mark certain data features, and you want to create a caption that reflects this. Here's an example where we'll use point size to indicate populations of California cities. We would like a legend that specifies the scale of the dimensions of the points, and we will achieve this by tracing data labeled without entries: import pandas as cities PD = pd.read_csv('data/california_cities.csv') # Extract the data we are interested in wide, lon = cities['latd'], cities['longd'] population, area = cities['population_total'], cities['area_total_km2'] # Scatter points, using size and color, but without label plt.scatter(lon, wide, label=None, c=np.log10(population), cmap='viridis',s=area, linewidth=0, alpha=0.5) plt.axis(aspect='equal') plt.xlabel('longitude') plt.ylabel('latitude') plt.colorbar(label=') plt.log$_{10}$(population)') plt.clim(3, 7) # Here we will create a caption: # we will draw empty lists with the desired size and label for the area in [100, 300, 500]: plt.scatter([], [], c='k' , alpha=0.3, s=area, label=str(area) + 'km$^2$') plt.legend(scatterpoints=1 , frameon=False, labelspacing=1, title='City Area') plt.title('California Cities: Area and Population'); The legend will always refer to an object that is on the plot, so if we want to display a certain shape, we have to plot it. In this case, the objects we want (grey circles) are not on the plot, so we forge them by drawing empty lists. Note also that the caption lists only graphic elements that have a specified label. By drawing empty lists, we create tagged plot objects that are taken over by the legend, and now our legend tells us some useful information. This strategy can be useful for creating more sophisticated views. Finally, note that for such geographic data, it would be clearer if we could show the state limits or other map-specific elements. For this, an excellent choice of tool is Matplotlib Basemap addon toolkit, which we will explore in geographic data with Basemap. Sometimes when you design a graph you want to add multiple captions to the same axes. Unfortunately, Matplotlib does not make this easy: through the legend standard interface, it is only possible to create a single legend for the entire plot. If you try to create a second caption using plt.legend() or ax.legend(), it will simply overwrite the first. We can solve this by creating a new legend artist from scratch, and then using the lower-level ax.add_artist() method to manually add the second artist to the plot: fig, ax = plt.subplots() lines = [] styles = ['-', '-', '-.', ':'] x = np.linspace(0, 10, 1000) for i in range(4): lines += ax.plot(x, np.sin(x - i * np.pi / 2), styles[i], color='black') # specify the lines and labels of the first legend ax.legend(lines[:2], ['line A', 'line B'], place='upper right', frameon=False) # Create the second legend and add the artist manually. Din Din import leg Legend = Legend(ax, lines[2:], ['line C', 'line D'], place='light-right', frameon=False) ax.add_artist(foot); This is a glimpse into the low-level artist objects that encompass any Matplotlib plot. If you examine the source code of ax.legend() (remember that you can do this with the IPython notebook using ax.legend??) you will see that the function simply consists of a logic to create a suitable legend artist, which is then saved in the attribute legend_ and added to the figure when the plot is drawn. matplotlib: Plot multiple columns of panda data, You can plot multiple columns at once by providing a list of column names to plot argument's y. This will produce a graph if the bars are sitting side by side. In order to overlay them, you will have to call the plot several times, and provide axes to plot to as an axe argument to the plot. You can plot multiple columns at once by providing a list of column names to the graphic argument 's y. df.plot(x=X, y= [A, B, C], kind=bar) This will produce a graph in which the bars sit side by side. Drawing with matplotlib, On DataFrame, the plot is a convenience to draw all columns with labels: For a DataFrame, hist plots histograms of columns on several subplots. Pandas bar plot Let's start with a basic bar plot first. We will take the Bar plot with several columns and before changing the backend matplotlib - it is most useful to shoot the plots in a separate window (using tk %matplotlib), so we will restart the kernel and use a GUI backend from here on out. Dataframe Pandas: Plot Examples with Matplotlib and Pyplot, After you know that v0.21.0rc1 gives a warning. UserWarning: Pandas does not allow you to create columns with a new attribute name. Instead, the Column Name column or list of names, or vector. Can be any entry valid to: str or str list: Optional: column in DataFrame to pandas. DataFrame.groupby(). A box plot will be made on the value of the columns in. str or matrix-like: Optional: axe: Matplotlib axes to be used by boxplot. Matplotlib.axex.Axes class object: The optional set legend for multi-line plot (in python), I find the easiest solution is to give the labels of the lines when creating. Try the following, you will see both lines appear on the caption: Try the following, you will see both lines appear on the caption: import matlotlib.pyplot as plt plt.plot( [1, 2, 3], color='red', label='line one') plt.plot( [4, 6, 8], color='blue', label='line two') plt.legend() plt.show() share. Share a link to this response. Copy link. Legend guide, line_up, = plt.plot([1, 2, 3], label='Line 2') line_down, = plt.plot([3, 2, 1], label='Line Sometimes it is clearer to divide the caption entries into multiple captions. Basic Matplotlib: Exercise-5 with solution. Write a Python to draw two or more lines on the same plot with matching captions of each line. Sample Solution: Customizing Plot Legends, plot() command is able to create multiple lines at once, date, returns a list of line instances created. Switching any of these to plt.legend() will tell you who to identify, plt.legend() method adds the legend to the plot. import matplotlib.pyplot as plt #Plot a line graph plt.plot ([5, 15], label='Rice') panda. DataFrame.plot.line, pandas. DataFrame.plot.line?. DataFrame.plot. line (auto, x=None, y=None, **kwargs)[source]?. Plot the Series or DataFrame as lines. This function is useful to plot pandas. DataFrame.plot.line? DataFrame.plot.line (auto, x = None, y = None, ** kwongs) [source] ? Series plotted or Frame data as lines. This function is useful for graphically representing lines by using DataFrame values as coordinates. Parameters x int or str, optional. Columns to use for the horizontal axis. Either the location or the label of the columns to use. Line plot using Pandas, In a Pandas line plot, the data frame index is plotted on the x axis. Currently, we have a value index of 0 to 15 per whole increment. We need to establish our date field to be the index of our data frame so that it is represented accordingly on the X. Panda axis. DataFrame.plot.line. DataFrame.plot.line(x=None, y=None, **kwds) [source] ?. Plot DataFrame columns as lines. This function is useful for graphically representing lines by using DataFrame values as coordinates. Parameters: x : int or str, optional. Columns to use for the horizontal axis. plot a DataFrame using Pandas, line chart; Bar chart; Pie chart. Plot a dot chart using Pandas. Scatter plots are used to describe a relationship between two variables. In the line plot In a Pandas, the data frame index is plotted on the x-axis. Currently, we have a value index of 0 to 15 per whole increment. Matplotlib plot several lines from arrayMatplotlib: to draw multiple lines from the columns of an array, but, so if you correctly want to apply the labels at once instead of typing them in each line. What you can do is save x = np.loadtxt('example_array.npy') plt.plot(x[:1:3], label = 'first 2 lines') plt.plot(x[:,3:5],label = '3rd and 4th lines') plt.legend() I get as many legend labels as i. So the above code produces four labels in the legend box. #122 multiple chart lines - Python Graph Gallery, Each line represents a set of values, for example, one set per group. To do this with matplotlib we only have to call the plot function several times (once on the application that gave rise to matplotlib is an EEG viewer, which must effectively handle hundreds of lines; this is available as part of the pbrain package. Here is an example of how this application does not plot multiline with instead gain changes. Pyplot, LineCollection allows one to draw multiple lines on a figure. to test the support for the masked matrix: segs = np.ma.masked_where((segs > 50) & (segs It's pretty easy to do this in the basic python plot using the matplotlib library. We start with the simple one, one line: import matplotlib.pyplot as plt plt.plot([1,2,3,4]) # when to give a label plt.xlabel(This is the label X) plt.ylabel(This is the label Y) plt.show() Pandas plot several seriesPandas: plot several series of Time DataFrame in one plot , Look at this variants. The first is Andrews' curves, and the second is a multiline plot that is grouped by a Moon column. The first data frame is Andrews' curves, and the second is a multiline plot that is grouped by a Moon column. Data in the data frame include three columns Temperature, Day and Month: import panda as pd import statsmodels.api as sm import matplotlib.pylab as plt from pandas.tools.plotting import andrews_curves data = sm.datasets.get_rdataset('airquality').data fig, (ax1, ax2) = plt.subplots(nrows = 2, ncols = 1) date = date[data.columns.tolist() [3:]] # use only Temp, Month, Day # Andrews ' Folding curves with matplotlib, Plot Method on Series and DataFrame is just a simple wrap around plt. plot: For a DataFrame, hist plots histograms of the columns on several pandas. Series.plot? Series.plot (self, * args, **kwongs) [source] ? Make serial plots or DataFrame. Uses the backend specified by the trace.backend option. By default, matplotlib is used. Data Series or DataFrame parameters. The object for which the method is called. x label or position, default None. Use only if the data is a Data Frame. View, plot method on Series and DataFrame is just a simple wrap around the plt. plot: For a DataFrame, hist plots histograms of the columns on several pandas. Series.plot.bar? Series.plot.bar (self, x = None, y = None, ** kwargs) [source] ? Vertical bar plot. A bar graphic is a graphical representation that presents categorical data with rectangular bars with lengths proportional to the values it represents. Pandas plot xlabelAdd x and y labels to a parcel pandas, set(xlabel =x label, ylabel = y label) . Alternatively, the x-axis index label is automatically set to the Index name, if it has one. so df2.index.name = pandas uses matplotlib for base dataframe plots. So if you use pandas for the basic plot you can use matplotlib for plot customization. However, I propose an alternative method here using seaborn, which allows more customization of the plot while not going into the basic level of matplotlib. Panda. DataFrame.plot, x : label or position, default None. y : label, position, or list of labels, positions, default None. Allows you to plot one column from another. It has one million and one methods, two of which are set_xlabel and set_ylabel. # Draw a graph with the panda and keep what is returned axe = df . plot ( kind = 'scatter', x = 'GDP_per_capita', y = 'life_expectancy') # Set scale x because otherwise it enters strange negative axis numbers. set_xlim (( 0 , 70000 )) # Set axis Axe. set_xlabel (GDP (per capita) # Set axis . set_ylabel ( Life expectancy at birth) panda. DataFrame.plot, xlabel, or position, default None. Use only if the data is a Data Frame. ilabel, position or list of labels; labels; default None. Allows you to trace a column from 1. df.plot(x='Corruption',y='Freedom', kind='scatter', color='R') There is also a pandas.plotting.table help function that creates a table from DataFrame or Series and adds it to a matplotlib axis instance. This function can accept keywords that the matplotlib table has. Pandas groupby bar plotBar graphic from dataframe groupby, copying data from the link and running df = pd.read_clipboard(). then using the code df = df.replace(np.nan,0) df pandas parcel datetime groupby distribution. 3. Pandas: Multiple plot bars from aggregated columns. 4. seaborn several variable group bar plot. 2. Panda. DataFrame.plot.bar, A bar graphic is a graphical representation that presents categorical data with rectangular bars with lengths proportional to the values it represents. A bar graphic displays comparisons If kind = 'bar' or 'barh', you can specify relative alignments for the bar's graphic layout by position keyword. From 0 (left/bottom-end) to 1 (right/up). Default is 0.5 (center) If the way = scatter and argument c is the name of a column of data frame, the values of that column are used to color each point. Dataframe Pandas: Plot Examples with Matplotlib and Pyplot, Problem: Group 2 columns of a panda dataframe. Then view the aggregated data using a graphical representation of the bar. Tip: Use the unstack Pandas keyword: Draws the values of a group in multiple columns. This is just a panda programming note that explains how to plot in a fast-moving way different categories contained in a multi-column groupby, generating a two-tier multiindex. Let's say you have a data set that contains credit card transactions, including: Because this type of data is not available for free for privacy reasons, we generated a fake data set using the Faker python library, which generates false data for you. Jupyter draws multiple lines#122 Multi-line chart ? Python Graph Gallery, To draw multiple lines on a plot is as easy as repeating plt.plot : In [1]:. # RUN ALL THE CODE BEFORE YOU START import numpy as np from IPython kernel of Jupyter notebook is putd to display plots of code in input cells. It works perfectly with the matplotlib library. The in-line option with magic function %matplotlib renders the graphic cell even if the show() function of the graphic object is not called. Matplotlib, You can pre-create an axis object using the pyplot matplotlibs package and then add the parcels to this axis object: import pandas as import pd Create multiple axes on the same figure and then make the figure in the notebook. For example: import pandas as pd import matplotlib.pyplot as plt %matplotlib inline ys = [[0,1,2,3,4],[4,3,2,1,0]] x_ax = [0,1,2,3,4] fig, axs = plt.subplots(ncols=2, 4)) for i, y_ax in enumeration(ys): pd. Series(y_ax, index=x_ax).plot(kind='bar', ax=axs[i]) axs[i].set_title('Plot number {}'.format(i+1)) Draw multiple lines in iPython/pandas Produce Multiple Plots , Matplotlib's Object-facing interface. A oriented object the interface is an interface in which the plot components (such as axis, title, lines, markers, check You can draw multiple lines in parallel by specifying the name of the lines in the constructor and by transmitting all values in a list. pp = ProgressPlot (line_names = [smooth, log, cos, sin], x_lim= [0, 1000], y_lim= [-1, 4]) for i in range (1000): pp.update ([ [i / 250, np.log10 (i + 1), np.cos (i / 100), np.sin (i / 100)]) pp.finalize () ()

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