How can we have (0,0) as below in plotly (python) in scatterplot using go.Scattergl().
How to have some padding before (0,0)?
You have to specify that the lower bound of the range of both axes is 0.
import plotly.graph_objects as go
import numpy as np
x = np.array([1, 2, 3, 4, 5])
fig = go.Figure(data=go.Scatter(x=x, y=x**2))
fig.update_layout(yaxis_range=[0,4])
fig.update_layout(xaxis_range=[0,4])
fig.show()
How to:
display symbols in the legend
colour markers in the same way as the errorbars (argument color gives an error: ValueError: RGBA sequence should have length 3 or 4
remove connecting lines - get only the scatter with errorbars
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D # for legend handle
fig, ax = plt.subplots(figsize = (10,10))
times = [1, 2, 3, 4, 5]
rvs = [2, 4, 2, 4, 7]
sigma = [0.564, 0.6, 0.8, 0.8, 0.4]
rv_telescopes = ['A', 'B', 'A', 'C', 'C']
d = {'rv_times': times, 'rv_rvs': rvs, 'rv_sigma': sigma, 'rv_telescopes': rv_telescopes }
df = pd.DataFrame(data=d)
colors = {'A':'#008f00', 'B':'#e36500', 'C':'red'}
plt.errorbar(df['rv_times'], df['rv_rvs'], df['rv_sigma'], marker = '_', ecolor = df['rv_telescopes'].map(colors), color = df['rv_telescopes'].map(colors), zorder = 1, ms = 30)
handles = [Line2D([0], [0], marker='_', color='w', markerfacecolor=v, label=k, markersize=10) for k, v in colors.items()]
ax.legend(handles=handles, loc='upper left', ncol = 2, fontsize=14)
plt.show()
After edit
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D # for legend handle
import pandas as pd
import numpy as np
times = [1, 2, 3, 4, 5]
rvs = [2, 4, 2, 4, 7]
sigma = [0.564, 0.6, 0.8, 0.8, 0.4]
rv_telescopes = ['A', 'B', 'A', 'C', 'C']
d = {'rv_times': times, 'rv_rvs': rvs, 'rv_sigma': sigma, 'rv_telescopes': rv_telescopes}
df = pd.DataFrame(data=d)
colors = {'A': '#008f00', 'B': '#e36500', 'C': 'red'}
fig, ax = plt.subplots(figsize=(10, 10))
ax.errorbar(df['rv_times'], df['rv_rvs'], df['rv_sigma'], color='none', ecolor=df['rv_telescopes'].map(colors) ,linewidth=1)
ax.scatter(df['rv_times'], df['rv_rvs'], marker='_', linewidth=3, color=df['rv_telescopes'].map(colors), s=1000)
for rv_teles in np.unique(df['rv_telescopes']):
color = colors[rv_teles]
df1 = df[df['rv_telescopes'] == rv_teles] # filter out rows corresponding to df['rv_telescopes']
ax.errorbar(df1['rv_times'], df1['rv_rvs'], df1['rv_sigma'],
color=color, ls='', marker='_', ms=30, linewidth=3, label=rv_teles)
ax.legend(loc='upper left', ncol=1, fontsize=14)
plt.show()
plt.errorbar() works very similar to plt.plot() with extra parameters. As such, it primarily draws a line graph, using a single color. The error bars can be given individual colors via the ecolor= parameter. The markers, however, get the same color as the line graph. The line graph can be suppressed via an empty linestyle. On top of that, plt.scatter() can draw markers with individual colors.
In order not the mix the 'object-oriented' with the 'functional interface', the following example code uses ax.errorbar() and ax.scatter().
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D # for legend handle
import pandas as pd
import numpy as np
times = [1, 2, 3, 4, 5]
rvs = [2, 4, 2, 4, 7]
sigma = [0.564, 0.6, 0.8, 0.8, 0.4]
rv_telescopes = ['A', 'B', 'A', 'C', 'C']
d = {'rv_times': times, 'rv_rvs': rvs, 'rv_sigma': sigma, 'rv_telescopes': rv_telescopes}
df = pd.DataFrame(data=d)
colors = {'A': '#008f00', 'B': '#e36500', 'C': 'red'}
fig, ax = plt.subplots(figsize=(10, 10))
ax.errorbar(df['rv_times'], df['rv_rvs'], df['rv_sigma'], color='none', ecolor=df['rv_telescopes'].map(colors))
ax.scatter(df['rv_times'], df['rv_rvs'], marker='_', color=df['rv_telescopes'].map(colors), s=100)
handles = [Line2D([0], [0], linestyle='', marker='_', color=v, label=k, markersize=10) for k, v in colors.items()]
ax.legend(handles=handles, loc='upper left', ncol=1, fontsize=14)
plt.show()
A far easier approach would be to call ax.errorbar() multiple times, once for each color. This would automatically create appropriate legend handles:
for rv_teles in np.unique(df['rv_telescopes']):
color = colors[rv_teles]
df1 = df[df['rv_telescopes'] == rv_teles] # filter out rows corresponding to df['rv_telescopes']
ax.errorbar(df1['rv_times'], df1['rv_rvs'], df1['rv_sigma'],
color=color, ls='', marker='_', ms=30, label=rv_teles)
ax.legend(loc='upper left', ncol=1, fontsize=14)
plt.show()
I want heatmap annotation as symbols. '*' at place of 1 and blank at 0.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
x = pd.DataFrame({'a':[1,0,1,0]})
fig, (ax) = plt.subplots(ncols=1)
sns.heatmap(x, cmap="BuPu",annot=True,fmt='g',annot_kws={'size':10},ax=ax, yticklabels=[], cbar=False, linewidths=.5,robust=True, vmin=0, vmax=1)
plt.show()
The heatmap can only annotate with numbers. To put other text (or unicode symbols), ax.text can be used. The center of each cell is at 0.5 added to both the row and the column number.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
x = pd.DataFrame({'a': [1, 0, 1, 0], 'b': [1, 1, 0, 1], 'c': [0, 1, 0, 0]})
fig, (ax) = plt.subplots(ncols=1)
sns.heatmap(x, cmap="BuPu", annot=False, ax=ax, yticklabels=[], cbar=False, linewidths=.5)
for i, c in enumerate(x.columns):
for j, v in enumerate(x[c]):
if v == 1:
ax.text(i + 0.5, j + 0.5, '★', color='gold', size=20, ha='center', va='center')
plt.show()
I am graphing my predicted and actual results of an ML project using pyplot. I have a scatter plot of each dataset as a subplot and the Y values are elements of [-1, 0, 1]. I would to change the color of the points if both points have the same X and Y value but am not sure how to implement this. Here is my code so far:
import matplotlib.pyplot as plt
Y = [1, 0, -1, 0, 1]
Z = [1, 1, 1, 1, 1]
plt.subplots()
plt.title('Title')
plt.xlabel('Timestep')
plt.ylabel('Score')
plt.scatter(x = [i for i in range(len(Y))], y = Y, label = 'Actual')
plt.scatter(x = [i for i in range(len(Y))], y = Z, label = 'Predicted')
plt.legend()
I would simply make use of NumPy indexing in this case. Specifically, first plot all the data points and then additionally highlight only those point which fulfill the condition X==Y and X==Z
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
Y = np.array([1, 0, -1, 0, 1])
Z = np.array([1, 1, 1, 1, 1])
X = np.arange(len(Y))
# Labels and titles here
plt.scatter(X, Y, label = 'Actual')
plt.scatter(X, Z, label = 'Predicted')
plt.scatter(X[X==Y], Y[X==Y], color='black', s=500)
plt.scatter(X[X==Z], Z[X==Z], color='red', s=500)
plt.xticks(X)
plt.legend()
plt.show()
I have this scatter plot:
I'd like to move the grid in a way that each point (green square) would be surrounded by the grid's cells. For example:
The code to reproduce the plot:
import matplotlib.pyplot as plt
data = [24, 24, 24, 16, 16, 2, 2, 2]
x = list(range(0, len(data)))
y = list(range(0, 25))
plt.scatter(x, data, marker='s', c='g', s=100)
plt.yticks(y)
plt.xticks(x)
plt.grid(True)
plt.show()
Maybe something like the following meets the requirement. You can use the minor ticks for the grid and the major ticks for the labels.
import numpy as np
import matplotlib.pyplot as plt
data = [24, 24, 24, 16, 16, 2, 2, 2]
x = list(range(0, len(data)))
fig, ax = plt.subplots()
ax.scatter(x, data, marker='s', c='g', s=49)
ax.set_yticks(np.arange(25))
ax.set_yticks(np.arange(25+1)-0.5, minor=True)
ax.set_xticks(np.arange(len(data)))
ax.set_xticks(np.arange(len(data)+1)-0.5, minor=True)
ax.grid(True, which="minor")
ax.set_aspect("equal")
plt.show()