I am trying to plot an array of 101 rows * 12 Columns, with row #1 as a highlight using the code below:
plt.plot(HW.transpose()[1:101],color = 'grey', alpha = 0.1)
plt.plot(HW.transpose()[0],color = 'red', linewidth = 3, alpha = 0.7)
The only issue in this graph is that 'S1' somehow ends up in the last instead of beginning. What am I doing wrong?
HW.transpose()[1:101] doesn't select the desired columns. You can use HW.transpose().iloc[:, 1:101] instead:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
HW = pd.DataFrame(np.random.randn(101, 12).cumsum(axis=1), columns=[f'S{i}' for i in range(1, 13)])
plt.plot(HW.transpose().iloc[:, 1:101], color='grey', alpha=0.1)
plt.plot(HW.transpose().iloc[:, 0], color='red', linewidth=3, alpha=0.7)
plt.show()
Related
I've been playing with Titanic dataset and working through some visualisations in Pandas using this tutorial. https://www.kdnuggets.com/2023/02/5-pandas-plotting-functions-might-know.html
I have a visual of scatterplot having used this code.
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('train.csv')
I was confused by bootstrap plot result so went on to scatterplot.
pd.plotting.scatter_matrix(df, figsize=(10,10), )
plt.show()
I can sort of interpret it but I'd like to put the various variables at top and bottom of every column. Is that doable?
You can use:
fig, ax = plt.subplots(4, 3, figsize=(20, 15))
sns.scatterplot(x = 'bedrooms', y = 'price', data = dataset, whis=1.5, ax=ax[0, 0])
sns.scatterplot(x = 'bathrooms', y = 'price', data = dataset, whis=1.5, ax=ax[0, 1])
I have a data frame with four columns I would like to plot the normality test for each column in a 2*2 grid, but it only plot one figure, and the else is empty.
import random
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2,2, figsize=(15, 6), facecolor='w', edgecolor='k')
fig.subplots_adjust(hspace = .5, wspace=.001)
data = {'col1': [random.randrange(1, 50, 1) for i in range(1000)], 'col2': [random.randrange(1, 50, 1) for i in range(1000)],'col3':[random.randrange(1, 50, 1) for i in range(1000)]
,'col4':[random.randrange(1, 50, 1) for i in range(1000)]}
df = pd.DataFrame(data)
for ax, d in zip(axs.ravel(), df):
ax=stats.probplot(df[d], plot=plt)
#ax.set_title(str(d))
plt.show()
is there a way to construct the subplot and the stats.probplot within a loop?
In your code, you need to change the for loop to this:
for ax, d in zip(axs.ravel(), df):
stats.probplot(df[d], plot=ax)
#ax.set_titl(str(d))
plt.show()
I hope this will help you move on.
I am trying to create a heatmap displaying correlation coefficient values. I'm quite new at this, but the code below would annotate in multiple decimal places, whereas i'm trying to narrow down to 2 d.p.
Does anyone have experience with this?
import pandas_datareader.data as web
import pandas as pd
import datetime as dt
import csv
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
import seaborn as sns
style.use('ggplot')
def visualize_data():
df = pd.read_csv('sti_joined.csv')
df.set_index('Date', inplace=True)
df_corr = df.pct_change().corr()
print(df_corr.head())
data = df_corr.values
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
# heatmap = ax.pcolor(data, cmap=plt.cm.get_cmap('RdYlGn'))
heatmap = ax.pcolor(data, cmap=plt.cm.RdYlGn)
fig.colorbar(heatmap)
ax.set_xticks(np.arange(data.shape[0]) + 0.5, minor=False)
ax.set_yticks(np.arange(data.shape[1]) + 0.5, minor=False)
ax.invert_yaxis()
ax.xaxis.tick_top()
for y in range(data.shape[0]):
for x in range(data.shape[1]):
plt.text(x + 0.5, y + 0.5, '%.4f' % data[y, x],
horizontalalignment='center',
verticalalignment='center',
)
column_labels = df_corr.columns
row_labels = df_corr.index
ax.set_xticklabels(column_labels)
ax.set_yticklabels(row_labels)
plt.xticks(rotation=90)
heatmap.set_clim(-1,1)
plt.tight_layout()
plt.show()
visualize_data()
Instead of '%.4f' % data[y, x], you can try using something like
'{0:.2f}'.format(data[y,x])
I want to show my Data in two (or more) stacked Bargraphs inkluding Errorbars. My Code leans on an working Example, but uses df`s at input instead of Arrays.
I tried to set the df-output to an array, but this will not work
from uncertain_panda import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
raw_data = {'': ['Error', 'Value'],'Stars': [3, 18],'Cats': [2,15],'Planets': [1,12],'Dogs': [2,16]}
df = pd.DataFrame(raw_data)
df.set_index('', inplace=True)
print(df)
N = 2
ind = np.arange(N)
width = 0.35
first_Value = df.loc[['Value'],['Cats','Dogs']]
second_Value = df.loc[['Value'],['Stars','Planets']]
first_Error = df.loc[['Error'],['Cats','Dogs']]
second_Error = df.loc[['Error'],['Stars','Planets']]
p1 = plt.bar(ind, first_Value, width, yerr=first_Error)
p2 = plt.bar(ind, second_Value, width, yerr=second_Error, bottom=first_Value)
plt.xticks(ind, ('Pets', 'Universe'))
plt.legend((p1[0], p2[0]), ('Cats', 'Dogs', 'Stars', 'Planets'))
plt.show()
I expect an output like this:
https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/bar_stacked.html#sphx-glr-gallery-lines-bars-and-markers-bar-stacked-py
Instead i get this error:
TypeError: only size-1 arrays can be converted to Python scalars
I would like to format my x axis with the legend values at the mid point of each bar whilst retaining the gender group identification. I'd like lower the gender groups to sit below the other xticklabels for clarity.
To this point, I've added xticks but actually labeling them correctly and neatly is proving trickier.
from itertools import chain, cycle
import logging
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
matplotlib.style.use("ggplot")
m = {"Males" : {"Yes": 2, "No": 8}}
w = {"Females": {"Yes": 3, "No": 7}}
data = {**m, **w}
df = DataFrame(data)
# relative freq table
df_ft = (df / df.sum() * 100).T
ax = plt.subplot(111)
df_ft.plot(ax=ax, kind="bar", ylim=(0, 90),
title="Would you prefer to work at home? (10 males, 10 females)",
rot=0)
plt.ylabel("Relative Frequency (%)")
midp = 0.125 # standard bar width/2
t_l = ax.get_xticks().tolist()
ticks = list(chain.from_iterable((t - midp, t + midp) for t in t_l))
ax.set_xticks(t_l + ticks)
plt.show()
The following might be what you're looking for.
from itertools import chain
import matplotlib
import matplotlib.pyplot as plt
from pandas import DataFrame
matplotlib.style.use("ggplot")
df = DataFrame({'Males': {'Yes': 2, 'No': 8}, 'Females': {'Yes': 3, 'No': 7}})
df_ft = (df / df.sum() * 100).T
ax = plt.subplot(111)
df_ft.plot(ax=ax, kind="bar", ylim=(0, 90),
title="Would you prefer to work at home? (10 males, 10 females)",
rot=0)
plt.ylabel("Relative Frequency (%)")
midp = 0.125 # standard bar width/2
t_l = ax.get_xticks().tolist()
ticks = list(chain.from_iterable((t - midp, t + midp) for t in t_l))
ax.set_xticks(t_l + ticks)
labels = [l for l in ax.get_xticklabels()]
for i,l in enumerate(labels[len(df_ft):]):
l.set_text(df_ft.columns[i % len(df_ft.columns)])
for i,l in enumerate(labels[:len(df_ft)]):
l.set_text("\n"+l.get_text())
ax.set_xticklabels(labels)
plt.savefig(__file__+".png")
plt.show()
Altair would do a great job here.
from altair import *
from pandas import DataFrame
df = DataFrame({'Males': {'Yes': 2, 'No': 8}, 'Females': {'Yes': 3, 'No': 7}})
df = df.stack().reset_index()
df.columns=['response','gender','count']
Vis #1
Chart(df).mark_bar().encode(x='gender',y='count',color='response').configure_cell(width=200, height=200)
Vis 2
Chart(df).mark_bar().encode(x=X('response', axis=False),
y=Y('count', axis=Axis(grid=False)),
color='response',
column=Column('gender', axis=Axis(axisWidth=1.0, offset=-8.0, orient='bottom'),scale=Scale(padding=30.0))).configure_cell(width=200, height=200).configure_facet_cell(strokeWidth=0)