I have this dataframe called cases_deaths:
week daily_case_totals daily_death_totals
0 1 2.0 0.0
1 2 12.0 0.0
2 3 12.0 0.0
3 4 2.0 0.0
4 5 573.0 6.0
5 6 3134.0 12.0
6 7 3398.0 32.0
7 8 992.0 25.0
.
.
.
And this code to generate to Seaborn charts:
fig, axes = plt.subplots(2, 1, figsize=(11, 10))
for name, ax in zip(['daily_case_totals', 'daily_death_totals'], axes):
sns.barplot(data=cases_deaths, x='week', y=name, ax=ax, color = 'red')
And the chart looks like this:
But I want the top one to be blue and bottom to be red. Not sure how to do that, I've tried passing in a list of colors to the color parameter in the for loop but that yielded an error.
Just add one more iterable to zip for the colors:
import seaborn as sns
fig, axes = plt.subplots(2, 1, figsize=(11, 10))
for name, color, ax in zip(('daily_case_totals', 'daily_death_totals'),
('blue', 'red'),
axes):
sns.barplot(data=cases_deaths, x='week', y=name, ax=ax, color=color)
Related
My dataframe looks like this:
date index count weekday_num max_temperature_C
0 2019-04-01 0 1379 0 18
1 2019-04-02 1 1395 1 21
2 2019-04-03 2 1155 2 19
3 2019-04-04 3 342 3 18
4 2019-04-05 4 216 4 14
I would like to plot count vs max_temperature_C and colour by weekday_num
I have tried the below:
#create the scatter plot of trips vs Temp
plt.scatter(comb2['count'], comb2['max_temperature_C'], c=comb2['weekday_num'])
# Label the axis
plt.xlabel('Daily Trip count')
plt.ylabel('Max Temp c')
plt.legend(['weekday_num'])
# Show it!
plt.show()
However I am not sure quite how to get the legend to display all of the colours which correspond to each of the 'weekday_num' ?
Thanks
You can use the automated legend creation like this:
fig, ax = plt.subplots()
scatter = ax.(comb2['count'], comb2['max_temperature_C'], c=comb2['weekday_num'])
# produce a legend with the unique colors from the scatter
legend = ax.legend(*scatter.legend_elements(),
loc="upper right", title="Weekday num")
ax.add_artist(legend)
plt.show()
I have a dataframe with information concerning the date and the cluster that it belongs (it was done before based on collected temperatures for each day). I want to plot this data in sequence, like a stacked bar chart, changing the color of each element according to the assigned cluster. Here it is my table (the info goes up to 100 days):
Date
order
ClusterNo2
constant
2020-08-07
1
3.0
1
2020-08-08
2
0.0
1
2020-08-09
3
1.0
1
2020-08-10
4
3.0
1
2020-08-11
5
1.0
1
2020-08-12
6
1.0
1
2020-08-13
7
3.0
1
2020-08-14
8
2.0
1
2020-08-15
9
2.0
1
2020-08-16
10
2.0
1
2020-08-17
11
2.0
1
2020-08-18
12
1.0
1
2020-08-19
13
1.0
1
2020-08-20
14
0.0
1
2020-08-21
15
0.0
1
2020-08-22
16
1.0
1
Obs: I can't simply group the data by cluster because the plot should be sequential. I thought writing a code to identify the number of elements of each cluster sequentially, but then I will face the same problem for plotting. Someone know how to solve this?
The expected result should be something like this (the numbers inside the bar representing the cluster, the x-axis the time in days and the bar width the number of observed days with the same cluster in order :
You could use the dates for the x-axis, the 'constant' column for the y-axis,
and the Cluster id for the coloring.
You can create a custom legend using a list of colored rectangles.
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import pandas as pd
import numpy as np
N = 100
df = pd.DataFrame({'Date': pd.date_range('2020-08-07', periods=N, freq='D'),
'order': np.arange(1, N + 1),
'ClusterNo2': np.random.randint(0, 4, N).astype(float),
'constant': 1})
df['ClusterNo2'] = df['ClusterNo2'].astype(int) # convert to integers
fig, ax = plt.subplots(figsize=(15, 3))
num_clusters = df['ClusterNo2'].max() + 1
colors = plt.cm.Set2.colors
ax.bar(x=range(len(df)), height=df['constant'], width=1, color=[colors[i] for i in df['ClusterNo2']], edgecolor='none')
ax.set_xticks(range(len(df)))
labels = ['' if i % 3 != 0 else day.strftime('%d\n%b %Y') if i == 0 or day.day <= 3 else day.strftime('%d')
for i, day in enumerate(df['Date'])]
ax.set_xticklabels(labels)
ax.margins(x=0, y=0)
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
legend_handles = [plt.Rectangle((0, 0), 0, 0, color=colors[i], label=f'{i}') for i in range(num_clusters)]
ax.legend(handles=legend_handles, title='Clusters', bbox_to_anchor=(1.01, 1.01), loc='upper left')
fig.tight_layout()
plt.show()
You could just plot a normal bar graph, with 1 bar corresponding to 1 day. If you make the width also 1, it will look as if the patches are contiguous.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import BoundaryNorm
# simulate data
total_datapoints = 16
total_clusters = 4
order = np.arange(total_datapoints)
clusters = np.random.randint(0, total_clusters, size=total_datapoints)
# map clusters to colors
cmap = plt.cm.tab10
bounds = np.arange(total_clusters + 1)
norm = BoundaryNorm(bounds, cmap.N)
colors = [cmap(norm(cluster)) for cluster in clusters]
# plot
fig, ax = plt.subplots()
ax.bar(order, np.ones_like(order), width=1, color=colors, align='edge')
# xticks
change_points = np.where(np.diff(clusters) != 0)[0] + 1
change_points = np.unique([0] + change_points.tolist() + [total_datapoints])
ax.set_xticks(change_points)
# annotate clusters
for ii, dx in enumerate(np.diff(change_points)):
xx = change_points[ii] + dx/2
ax.text(xx, 0.5, str(clusters[int(xx)]), ha='center', va='center')
ax.set_xlabel('Time (days)')
plt.show()
I wish to create a (2x3) stacked barchart subplot from results using a groupby.size command, let me explain. I have a list of dataframes: list_df = [df_2011, df_2012, df_2013, df_2014, df_2015, df_2016]. A small example of these df's would be:
... Create Time Location Area Id Beat Priority ... Closed Time
2011-01-01 00:00:00 ST&SAN PABLO AV 1.0 06X 1.0 ... 2011-01-01 00:28:17
2011-01-01 00:01:11 ST&HANNAH ST 1.0 07X 1.0 ... 2011-01-01 01:12:56
.
.
.
(can only add a few columns as the layout messes up)
I'm using a groupby.size command to get a required count of events for these databases, see below:
list_df = [df_2011, df_2012, df_2013, df_2014, df_2015, df_2016]
for i in list_df:
print(i.groupby(['Beat', 'Priority']).size())
print(' ')
Producing:
Beat Priority
01X 1.0 394
2.0 1816
02X 1.0 644
2.0 1970
02Y 1.0 661
2.0 2309
03X 1.0 857
2.0 2962
.
.
.
I wish to identify which is the top 10 TOTALS using the beat column. So for e.g. the totals above are:
Beat Priority Total for Beat
01X 1.0 394
2.0 1816 2210
02Y 1.0 661
2.0 2309 2970
03X 1.0 857
2.0 2962 3819
.
.
.
So far I have used plot over my groupby.size but it hasn't done the collective total as I described above. Check out below:
list_df = [df_2011, df_2012, df_2013, df_2014, df_2015, df_2016]
fig, axes = plt.subplots(2, 3)
for d, i in zip(list_df, range(6)):
ax = axes.ravel()[i];
d.groupby(['Beat', 'Priority']).size().nlargest(10).plot(ax=ax, kind='bar', figsize=(15, 7), stacked=True, legend=True)
ax.set_title(f"Top 10 Beats for {i+ 2011}")
plt.tight_layout()
I wish to have the 2x3 subplot layout, but with stacked barcharts like this one I have done previously:
Thanks in advance. This has been harder than I thought it would be!
The data series need to be the columns, so you probably want
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
# create fake input data
ncols = 300
list_df = [pd.DataFrame({'Beat': np.random.choice(['{:02d}X'.format(i) for i in range(15)], ncols),
'Priority': np.random.choice(['1', '2'], ncols),
'othercolumn1': range(ncols),
'othercol2': range(ncols),
'year': [yr] * ncols}) for yr in range(2011, 2017)]
In [22]: print(list_df[0].head(5))
Beat Priority othercolumn1 othercol2 year
0 06X 1 0 0 2011
1 05X 1 1 1 2011
2 04X 1 2 2 2011
3 01X 2 3 3 2011
4 00X 1 4 4 2011
fig, axes = plt.subplots(2, 3)
for i, d in enumerate(list_df):
ax = axes.flatten()[i]
dplot = d[['Beat', 'Priority']].pivot_table(index='Beat', columns='Priority', aggfunc=len)
dplot = (dplot.assign(total=lambda x: x.sum(axis=1))
.sort_values('total', ascending=False)
.head(10)
.drop('total', axis=1))
dplot.plot.bar(ax=ax, figsize=(15, 7), stacked=True, legend=True)
I have plotted a stacked bar chart (see here: https://imgur.com/a/ESJeHuF), formed out of the dataframe below.
condition1 condition2 condition3
timestamp
2019-10-30 01:41:43 1.0 4.0 0.0
2019-10-30 01:50:11 1.0 2.0 4.0
2019-10-30 01:50:59 1.0 2.0 4.0
2019-10-30 01:51:36 1.0 2.0 4.0
2019-10-30 01:52:27 1.0 3.0 4.0
2019-10-30 01:53:10 2.0 4.0 0.0
2019-10-31 02:25:14 5.0 0.0 0.0
2019-10-31 04:15:54 5.0 0.0 0.0
I would like the colors in the bar chart to match their corresponding values in the dataframe via this color list:
color_list = ['r', 'g', 'b', 'm', 'k', 'k']
(e.g. if a value for the 2nd to last timestep is 5, to color the segment of the stacked bar chart as 'k', with that behavior repeated for all segment of the stacked bar chart columns.
The code below plots the stacked bars, however miscolors them (the link above shows this). It only assigns the first three colors to all of the values, where there are more corresponding colors/values in the Dataframe. The correct plot should have the timestamps on the x-axis, and the segments of the bars for each condition the correct colors.
fig = plt.figure()
ax = fig.add_subplot(111)
df.plot.bar(stacked=True, rot=1, legend=False, ax=fig.gca(), colors=color_list)
I would greatly appreciate any help, thank you in advance.
I don't know how important to you is the choice of colors.
I've just found a solution that seems to fix your problem, the only "but" is that is the development is easier if you accept one of the color schema's available. Othewrise, if you will have to make a colormap by hand, you can find examples with LinearSegmentedColormap from matplotlib.colors.
The code:
import matplotlib.pyplot as plt
from matplotlib.cm import ScalarMappable
data_color = [0.,1.,2.,3.,4.,5.] #data range from conditions columns
data_color = [x / max(data_color) for x in data_color]
custom_map = plt.cm.get_cmap('Accent') #one of the color schemas stored
custom = custom_map(data_color) #mapping the color info to the variable custom
fig = plt.figure()
ax = fig.add_subplot(111)
df.plot.bar(stacked=True, rot=1, legend=False, ax=fig.gca(), color=custom)
plt.show()
The display:
I have a dataframe,df with 29 rows by 24 columns dimension
Index 0.0 5.0 34.0 ... 22.0
2017-08-03 00:00:00 10 0 10 0
2017-08-04 00:00:00 20 60 1470 20
2017-08-05 00:00:00 0 58 0 24
2017-08-06 00:00:00 0 0 480 24
2017-09-07 00:00:00 0 0 0 25
: : : : :
: : : : :
2017-09-30 00:00:00
I intend to label bins for each subplot representing a column in the histogram chart.I have been able to draw the histogram in each subplot for each column using this code
fig = plt.figure(figsize = (15,20))
ax = fig.gca()
#Initialize the figure
plt.style.use('seaborn-darkgrid')
df.hist(ax = ax)
However, the labels of the bins of each subplot are far apart and bin labels are not explicitly specified by ranges on the x-axis which is difficult to interpret. I have looked at
Aligning bins to xticks in plt.hist but it doesnt explicitly solve for labelling bins when subplots are concerned. Any help will be great...
I have also tried this but i get ValueError: too many values to unpack (expected 2)
x=[0,40,80,120,160,200,240,280,320]
fig = plt.figure(figsize = (15,20))
ax = fig.gca()
# Initialize the figure
plt.style.use('seaborn-darkgrid')
n,bins= plt.hist(df,bins= x)
#labels & axes
plt.locator_params(nbins=8, axis='x')
plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
plt.title('Daily occurrence',fontsize=16)
plt.xlabel('Number of occurrence',fontsize=12)
plt.ylabel('Frequency',fontsize=12)
plt.xticks(x)
plt.xlim(0,320)