I am trying to label a scatter/bubble chart I create from matplotlib with entries from a column in a pandas data frame. I have seen plenty of examples and questions related (see e.g. here and here). Hence I tried to annotate the plot accordingly. Here is what I do:
import matplotlib.pyplot as plt
import pandas as pd
#example data frame
x = [5, 10, 20, 30, 5, 10, 20, 30, 5, 10, 20, 30]
y = [100, 100, 200, 200, 300, 300, 400, 400, 500, 500, 600, 600]
s = [5, 10, 20, 30, 5, 10, 20, 30, 5, 10, 20, 30]
users =['mark', 'mark', 'mark', 'rachel', 'rachel', 'rachel', 'jeff', 'jeff', 'jeff', 'lauren', 'lauren', 'lauren']
df = pd.DataFrame(dict(x=x, y=y, users=users)
#my attempt to plot things
plt.scatter(x_axis, y_axis, s=area, alpha=0.5)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.annotate(df.users, xy=(x,y))
plt.show()
I use a pandas datframe and I somehow get a KeyError- so I guess a dict() object is expected? Is there any other way to label the data using with entries from a pandas data frame?
You can use DataFrame.plot.scatter and then select in loop by DataFrame.iat:
ax = df.plot.scatter(x='x', y='y', alpha=0.5)
for i, txt in enumerate(df.users):
ax.annotate(txt, (df.x.iat[i],df.y.iat[i]))
plt.show()
Jezreal's answer is fine, but i will post this just to show what i meant with df.iterrows in the other thread.
I'm afraid you have to put the scatter (or plot) command in the loop as well if you want to have a dynamic size.
df = pd.DataFrame(dict(x=x, y=y, s=s, users=users))
fig, ax = plt.subplots(facecolor='w')
for key, row in df.iterrows():
ax.scatter(row['x'], row['y'], s=row['s']*5, alpha=.5)
ax.annotate(row['users'], xy=(row['x'], row['y']))
Related
I am using matplotlib.pyplot to make a histogram. Due to the distribution of the data, I want manually set up the bins. The details are as follows:
Any value = 0 in one bin;
Any value > 60 in the last bin;
Any value > 0 and <= 60 are in between the bins described above and the bin size is 5.
Could you please give me some help? Thank you.
I'm not sure what you mean by "the bin size is 5". You can either plot a histogramm by specifying the bins with a sequence:
import matplotlib.pyplot as plt
data = [0, 0, 1, 2, 3, 4, 5, 6, 35, 60, 61, 82, -5] # your data here
plt.hist(data, bins=[0, 0.5, 60, max(data)])
plt.show()
But the bin size will match the corresponding interval, meaning -in this example- that the "0-case" will be barely visible:
(Note that 60 is moved to the last bin when specifying bins as a sequence, changing the sequence to [0, 0.5, 59.5, max(data)] would fix that)
What you (probably) need is first to categorize your data and then plot a bar chart of the categories:
import matplotlib.pyplot as plt
import pandas as pd
data = [0, 0, 1, 2, 3, 4, 5, 6, 35, 60, 61, 82, -5] # your data here
df = pd.DataFrame()
df['data'] = data
def find_cat(x):
if x == 0:
return "0"
elif x > 60:
return "> 60"
elif x > 0:
return "> 0 and <= 60"
df['category'] = df['data'].apply(find_cat)
df.groupby('category', as_index=False).count().plot.bar(x='category', y='data', rot=0, width=0.8)
plt.show()
Output:
building off Tranbi's answer, you could specify the bin edges as detailed in the link they shared.
import matplotlib.pyplot as plt
import pandas as pd
data = [0, 0, 1, 2, 3, 4, 5, 6, 35, 60, 61, 82, -6] # your data here
df = pd.DataFrame()
df['data'] = data
bin_edges = [-5, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65]
bin_edges_offset = [x+0.000001 for x in bin_edges]
plt.figure()
plt.hist(df['data'], bins=bin_edges_offset)
plt.show()
histogram
IIUC you want a classic histogram for value between 0 (not included) and 60 (included) and add two bins for 0 and >60 on the side.
In that case I would recommend plotting the 3 regions separately:
import matplotlib.pyplot as plt
data = [0, 0, 1, 2, 3, 4, 5, 6, 35, 60, 61, 82, -3] # your data here
fig, axes = plt.subplots(1,3, sharey=True, width_ratios=[1, 12, 1])
fig.subplots_adjust(wspace=0)
# counting 0 values and drawing a bar between -5 and 0
axes[0].bar(-5, data.count(0), width=5, align='edge')
axes[0].xaxis.set_visible(False)
axes[0].spines['right'].set_visible(False)
axes[0].set_xlim((-5, 0))
# histogram between (0, 60]
axes[1].hist(data, bins=12, range=(0.0001, 60.0001))
axes[1].yaxis.set_visible(False)
axes[1].spines['left'].set_visible(False)
axes[1].spines['right'].set_visible(False)
axes[1].set_xlim((0, 60))
# counting values > 60 and drawing a bar between 60 and 65
axes[2].bar(60, len([x for x in data if x > 60]), width=5, align='edge')
axes[2].xaxis.set_visible(False)
axes[2].yaxis.set_visible(False)
axes[2].spines['left'].set_visible(False)
axes[2].set_xlim((60, 65))
plt.show()
Output:
Edit: If you wanna plot probability density, I would edit the data and simply use hist:
import matplotlib.pyplot as plt
data = [0, 0, 1, 2, 3, 4, 5, 6, 35, 60, 61, 82, -3] # your data here
data2 = []
for el in data:
if el < 0:
pass
elif el > 60:
data2.append(61)
else:
data2.append(el)
plt.hist(data2, bins=14, density=True, range=(-4.99,65.01))
plt.show()
I'm developing in Python using the pandas, numpy and matplotlib modules, to paint various subplots of a dataframe, using the following code:
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker
data = {'Name': ['Status', 'Status', 'HMI', 'Allst', 'Drvr', 'CurrTUBand', 'RUSource', 'RUReqstrPriority', 'RUReqstrSystem', 'RUResReqstStat', 'CurrTUBand', 'DSP', 'SetDSP', 'SetDSP', 'DSP', 'RUSource', 'RUReqstrPriority', 'RUReqstrSystem', 'RUResReqstStat', 'Status', 'Delay', 'Status', 'Delay', 'HMI', 'Status', 'Status', 'HMI', 'DSP'],
'Value': [4, 4, 2, 1, 1, 1, 0, 7, 0, 4, 1, 1, 3, 0, 3, 0, 7, 0, 4, 1, 0, 1, 0, 1, 4, 4, 2, 3],
'Id_Par': [0, 0, 0, 0, 0, 0, 10, 10, 10, 10, 10, 0, 0, 22, 22, 28, 28, 28, 28, 0, 0, 38, 38, 0, 0, 0, 0, 0]
}
signals_df = pd.DataFrame(data)
def plot_signals(signals_df):
# Count signals by parallel
signals_df['Count'] = signals_df.groupby('Id_Par').cumcount().add(1).mask(signals_df['Id_Par'].eq(0), 0)
# Subtract Parallel values from the index column
signals_df['Sub'] = signals_df.index - signals_df['Count']
id_par_prev = signals_df['Id_Par'].unique()
id_par = np.delete(id_par_prev, 0)
signals_df['Prev'] = [1 if x in id_par else 0 for x in signals_df['Id_Par']]
signals_df['Final'] = signals_df['Prev'] + signals_df['Sub']
# Convert and set Subtract to index
signals_df.set_index('Final', inplace=True)
# Get individual names and variables for the chart
names_list = [name for name in signals_df['Name'].unique()]
num_names_list = len(names_list)
# Creation Graphics
fig, ax = plt.subplots(nrows=num_names_list, figsize=(10, 10), sharex=True)
plt.xticks(color='SteelBlue', fontweight='bold')
# Matplotlib's categorical feature to convert x-axis values to string
x_values = [-1, ]
for name in all_names_list:
x_values.append(signals_df[signals_df["Name"] == name]["Value"].index.values[0])
x_values.append(len(signals_df) - 1)
x_values = [str(i) for i in sorted(set(x_values))]
print(x_values)
for pos, (a_, name) in enumerate(zip(ax, names_list)):
# Creating a dummy plot and then remove it
dummy, = ax[pos].plot(x_values, np.zeros_like(x_values))
dummy.remove()
# Get data
data = signals_df[signals_df["Name"] == name]["Value"]
# Get values axis-x and axis-y
x_ = np.hstack([-1, data.index.values, len(signals_df) - 1])
y_ = np.hstack([0, data.values, data.iloc[-1]])
# Plotting the data by position
ax[pos].plot(x_.astype('str'), y_, drawstyle='steps-post', marker='*', markersize=8, color='k', linewidth=2)
ax[pos].set_ylabel(name, fontsize=8, fontweight='bold', color='SteelBlue', rotation=30, labelpad=35)
ax[pos].yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
ax[pos].yaxis.set_tick_params(labelsize=6)
ax[pos].grid(alpha=0.4, color='SteelBlue')
# Labeling the markers with CAN-Values
for i in range(len(y_)):
if i == 0:
xy = [x_[0].astype('str'), y_[0]]
else:
xy = [x_[i - 1].astype('str'), y_[i - 1]]
ax[pos].text(x=xy[0], y=xy[1], s=str(xy[1]), color='k', fontweight='bold', fontsize=12)
plt.show()
plot_signals(signals_df)
I'm having trouble when names get repeated, using Matplotlib's categorical feature and converting x-axis values to string; taking into consideration the focus of the answer; this is what is bringing me:
I have been trying to change the pandas conditions, since it is the condition that I am using in this line: x_values.append(signals_df[signals_df["Name"] == name]["Value"].index.values[0]) and when I print the variable x_values it brings me the wrong indices: ['-1', '0', '2', '3', '4', '5', '6', '11', '12', '20', '27'] and I can't make it work well.
I expect to achieve is a graph like the following:
The yellow shading is the jumps that it must make on the x-axis and that it are not painting on the y-axis. Thank you very much to anyone who can help me, any comments help.
I leave this answer for possible searches later for someone with the same topic. I found my error, the way I was handling the for loop was not correct, I replaced it and modified it as follows:
# Matplotlib's categorical feature and to convert x-axis values to string
x_values = [-1,]
x_values + = (list (set (can_signals.index)))
x_values = [str (i) for i in sorted (x_values)]
This now allows to bring up the graph as below:
My legend now shows,
I want to add my label in legend, from 0 to 7, but I don't want to add a for-loop in my code and correct each label step by step, my code like that,
fig, ax = plt.subplots()
ax.set_title('Clusters by OPTICS in 2D space after PCA')
ax.set_xlabel('First Component')
ax.set_ylabel('Second Component')
points = ax.scatter(
pca_2_spec[:,0],
pca_2_spec[:,1],
s = 7,
marker='o',
c = pred_pca_2_spec,
cmap= 'rainbow')
ax.legend(*points.legend_elements(), title = 'cluster')
plt.show()
Assuming pred_pca_2_spec is some np.array with values [0, 5, 10, 15, 20, 30, 35] to change the values of these to be in the range 0-7, simply divide (each element) by 5.
Sample Data:
import numpy as np
from matplotlib import pyplot as plt
np.random.seed(54)
pca_2_spec = np.random.randint(-100, 300, (100, 2))
pred_pca_2_spec = np.random.choice([0, 5, 10, 15, 20, 25, 30, 35], 100)
Plotting Code:
fig, ax = plt.subplots()
ax.set_title('Clusters by OPTICS in 2D space after PCA')
ax.set_xlabel('First Component')
ax.set_ylabel('Second Component')
points = ax.scatter(
pca_2_spec[:, 0],
pca_2_spec[:, 1],
s=7,
marker='o',
c=pred_pca_2_spec / 5, # Divide By 5
cmap='rainbow')
ax.legend(*points.legend_elements(), title='cluster')
plt.show()
I am trying to plot the line for a set of points. Currently, I have set of points as Column names X, Y and Type in the form of a data frame. Whenever the type is 1, I would like to plot the points as dashed and whenever the type is 2, I would like to plot the points as a solid line.
Currently, I am using for loop to iterate over all points and plot each point using plt.dash. However, this is slowing down my run time since I want to plot more than 40000 points.
So, is an easy way to plot the line overall points with different line dash type?
You could realize it by drawing multiple line segments like this
(Bokeh v1.1.0)
import pandas as pd
from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource, Range1d, LinearAxis
line_style = {1: 'solid', 2: 'dashed'}
data = {'name': [1, 1, 1, 2, 2, 2, 1, 1, 1, 1],
'counter': [1, 2, 3, 3, 4, 5, 5, 6, 7, 8],
'score': [150, 150, 150, 150, 150, 150, 150, 150, 150, 150],
'age': [20, 21, 22, 22, 23, 24, 24, 25, 26, 27]}
df = pd.DataFrame(data)
plot = figure(y_range = (100, 200))
plot.extra_y_ranges = {"Age": Range1d(19, 28)}
plot.add_layout(LinearAxis(y_range_name = "Age"), 'right')
for i, g in df.groupby([(df.name != df.name.shift()).cumsum()]):
source = ColumnDataSource(g)
plot.line(x = 'counter', y = 'score', line_dash = line_style[g.name.unique()[0]], source = source)
plot.circle(x = 'counter', y = 'age', color = "blue", size = 10, y_range_name = "Age", source = source)
show(plot)
I have a dataset with about 9800 entries. One column contains user names (about 60 individual user names). I want to generate a scatter plot in matplotlib and assign different colors to different users.
This is basically what I do:
import matplotlib.pyplot as plt
import pandas as pd
x = [5, 10, 20, 30, 5, 10, 20, 30, 5, 10, 20, 30]
y = [100, 100, 200, 200, 300, 300, 400, 400, 500, 500, 600, 600]
users =['mark', 'mark', 'mark', 'rachel', 'rachel', 'rachel', 'jeff', 'jeff', 'jeff', 'lauren', 'lauren', 'lauren']
#this is how the dataframe basicaly looks like
df = pd.DataFrame(dict(x=x, y=y, users=users)
#I go on an append the df with colors manually
#I'll just do it the easy albeit slow way here
colors =['red', 'red', 'red', 'green', 'green', 'green', 'blue', 'blue', 'blue', 'yellow', 'yellow', 'yellow']
#this is the dataframe I use for plotting
df1 = pd.DataFrame(dict(x=x, y=y, users=users, colors=colors)
plt.scatter(df1.x, df1.y, c=df1.colors, alpha=0.5)
plt.show()
However, I don't want to assign colors to the users manually. I have to do this many times in the coming weeks and the users are going to be different every time.
I have two questions:
(1) Is there a way to assign colors automatically to the individual users?
(2) If so, is there a way to assign a color scheme or palette?
user_colors = {}
unique_users = list(set(users))
step_size = (256**3) // len(unique_users)
for i, user in enumerate(unique_users):
user_colors[user] = '#{}'.format(hex(step_size * i)[2:])
Then you've got a dictionary (user_colors) where each user got one unique color.
colors = [user_colors[user] for user in users]
Now you've got your array with a distinct color for each user