I want to visualize the interactions between two data's. Original (green) and prediction (brown). The length of the top line is from 200 to -20 and bottom from -20 to 200.
For the above table, I want to visualize in the below format
code I have tried so far
def newline(p1, p2, color='black'):
ax = plt.gca()
fig, ax = plt.subplots(1,1,figsize=(5,5), dpi= 60)
ax.hlines(y=1, xmin=-20, xmax=200, color='black', alpha=0.7)
ax.hlines(y=1.1, xmin=-20, xmax=200, color='black', alpha=0.7)
plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = True
plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
ax.yaxis.set_visible(False)
plt.box(False)
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
def newline(p1, p2, color='black'):
ax = plt.gca()
df = pd.read_excel('visual.xlsx', sheet_name='Sheet2')
prediction_list = df['prediction'].tolist()
originale_list = df['original'].tolist()
result_prediction = []
result_original = []
for i in range(len(prediction_list)):
temp = []
test = prediction_list[i].split('&')
for j in range(len(test)):
temp = []
temp.append(200 - int(test[j].split(':')[0]))
temp.append(int(test[j].split(':')[1]))
result_prediction.append(temp)
for i in range(len(originale_list)):
temp = []
test = originale_list[i].split('&')
for j in range(len(test)):
temp = []
temp.append(200 - int(test[j].split(':')[0]))
temp.append(int(test[j].split(':')[1]))
result_original.append(temp)
fig, ax = plt.subplots(1,1,figsize=(20,5), dpi= 60)
ax.hlines(y=1, xmin=200, xmax=-20, color='black', alpha=0.7)
ax.hlines(y=2, xmin=200, xmax=-20, color='black', alpha=0.7) -20')
for i in range(len(result_prediction)):
plt.plot(result_prediction[i], [2, 1], color='brown')
for i in range(len(result_original)):
plt.plot(result_original[i], [2, 1], color='green')
plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = True
plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True
ax.yaxis.set_visible(False)
plt.box(False)
plt.savefig('test.png')
plt.show()
That will be help you.
Thanks
Related
I try to plot lines in a loop,but its connecting it,i tried many variants,but cand understand and find the answer,maybe the dataframe
im a newbie in Matplotlib
the code of method:
self.testnewnewventnest[Debit] - is a nested dict with the data i need for plotting
def showmainplot(self):
for Debit in self.Debitlist:
self.Presinit = self.VentTable.loc[Debit]
self.Tinit= float(self.Tinit)
self.Presinit=int(float(self.Presinit))
self.Powinit = float(self.Powinit)
x = symbols("x")
for Turatie in self.Tfin:
eqPres = (Turatie/self.Tinit)*(Turatie/self.Tinit)*self.Presinit-x
PresFin = solve(eqPres)
eqDebit = (Turatie/self.Tinit)*int(Debit)
DebitFin = solve(eqDebit)
eqPow = (Turatie/self.Tinit)*(Turatie/self.Tinit)*(Turatie/self.Tinit)*float(self.Powinit)
self.TestnewVentnest['KW'] = float(eqPow)
self.TestnewVentnest['Turatie'] = Turatie
self.TestnewVentnest['Presiune'] = float(PresFin[0])
self.TestnewVent[float(eqDebit)] = dict(self.TestnewVentnest)
self.testnewnewventnest[Debit] = dict(self.TestnewVent)
print(self.testnewnewventnest)
axeslist = []
n=0
fig, ax = plt.subplots(figsize=(5, 5))
ax1 = ax.twinx()
ax1.spines.right.set_position(("axes", 1.06))
ax.set_xlabel("Debit")
for dicts in self.testnewnewventnest:
Ventdataframe = pd.DataFrame(self.testnewnewventnest[dicts])
print(Ventdataframe)
ax2 = plt.subplot()
fig, ax = plt.subplots(figsize=(5, 5))
ax1 = ax.twinx()
ax1.spines.right.set_position(("axes", 1.06))
ax.set_xlabel("Debit")
axeslist.append(plt.subplot())
# print(df.iloc[0])
# ax1.set_ylabel("Turatie")
# ax.set_ylabel("Presiune")
# Ventdataframe.plot(Ventdataframe.loc["Presiune"], color="b",label="Presiune"+str(n),marker = 'o')
Ventdataframe.loc["Presiune"].plot(color="b",label="Presiune"+str(n),marker = 'o')
n+=1
# ax2 = ax.twinx()
# ax2.set_ylabel('KW')
# ax1.plot(Ventdataframe.loc["Turatie"],color='#000000',label="Turatie",marker = 'o')
# ax2.plot(Ventdataframe.loc["KW"], color='r',label="KW",marker = 'o')
# ax1.grid()
# ax2.yaxis.set_major_locator(FixedLocator(Ventdataframe.loc["KW"]))
# ax.yaxis.set_major_locator(FixedLocator(Ventdataframe.loc["Presiune"]))
# ax1.yaxis.set_major_locator(FixedLocator(self.Tfin))
# ax.xaxis.set_major_locator(FixedLocator(Ventdataframe.columns))
# lc = matpl.ticker.NullLocator()
# ax.yaxis.set_major_locator(lc)
plt.show()
and the self.testnewnewventnest look like:
Yes,the problem was in the loop,and in the dictionaries,in every iteration he added all previous dictionaries from iterations
I would put that in a dataframe and do it like this.
uniques = df['ID'].unique()
for i in uniques:
fig, ax = plt.subplots()
fig.set_size_inches(4,3)
df_single = df[df['ID']==i]
sns.lineplot(data=df_single, x='Month', y='Expense')
ax.set(xlabel='Time', ylabel='Total Expense')
plt.xticks(rotation=45)
plt.show()
I tried to plot error bar with Matplotlib like graphic attached, I can't made it, any suggestion?
import numpy as np
import matplotlib.pyplot as plt
Media = data["Media"]
Periodo = data["Periodo"]
P10th = data["P10th"]
P90th = data["P90th"]
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
fig, ax = plt.subplots()
ax.errorbar(Media, P90th, P10th, color='red', ls='--', marker='o', capsize=5, capthick=1, ecolor='black')
plt.xticks(ind, ('1910-1940', '1950-1990', '1990-2000', '2001-2010') )
ax.set_ylim(ylims)
, please can you help me.
This is my output
Here's the plot for your data:
p_10 = [.19,.62, .77, 1]
p_90 = [7.19, 6.67, 7.36, 8.25]
M = [1.16, 2.06, 2.17, 2.52]
fig = plt.figure()
x = [1, 2, 3, 4]
y = M
yerr = [p_10, # 'down' error
p_90] # 'up' error
plt.errorbar(x, y, yerr=yerr, capsize=3, fmt="r--o", ecolor = "black")
Matplotlib axis with two scales shared origin
I have already tried to implement this the existing stackflow solution and my both x-axes are not aligning to 0.
My Code :
def align_xaxis(ax1, v1, ax2, v2):
"""adjust ax2 xlimit so that v2 in ax2 is aligned to v1 in ax1"""
x1, _ = ax1.transData.transform((v1, 0))
x2, _ = ax2.transData.transform((v2, 0))
inv = ax2.transData.inverted()
dx, _ = inv.transform((0, 0)) - inv.transform((x1-x2, 0))
minx, maxx = ax1.get_xlim()
ax2.set_xlim(minx+dx, maxx+dx)
def unrealized_profit_loss_graph(profit_loss):
plt.style.use('ggplot');
fig = plt.figure()
ax1 = fig.add_subplot(111);
ax2 = ax1.twiny()
profit_loss['total_G/l'].
plot(kind='barh',color=profit_loss.positive.map({True: 'g', False: 'r'}))
profit_loss['gain_loss_perc'].plot(kind='barh',color=profit_loss.positive.map({True: 'b', False: 'y'}))
ax1.set_xlabel('%', fontsize=12)
ax2.set_xlabel('$', fontsize=12);
align_xaxis(ax1,0,ax2,0)
plt.xlim(-5000, 20000)
plt.xticks(rotation=45);
plt.show();
I would like both x axes to align at 0.
Also to show the negative plus the positive of the ax1.
Working example :
def unrealized_profit_loss():
Profit_loss = Path("C:/Users/champ/Documents/Pers/Python/stock_dfs/Profit_loss_tranactions.xlsx")
df = pd.read_excel(Profit_loss, sheet_name='Unrealized')
current_prices_ROTH=price_data_ROTH.loc[price_data_ROTH.index[-1]] current_prices_Personal=price_data_Personal.loc[price_data_Personal.index[-1]]
df2 = pd.DataFrame({'Symbol':current_prices_ROTH.index, 'Prices':current_prices_ROTH.values})
df2 = pd.DataFrame({'Symbol':current_prices_Personal.index, 'Prices':current_prices_Personal.values})
da= pd.merge(df,df2, how='left',on=['Symbol','Symbol'])
da['gain_loss_perc']=round(((da['Prices']-da['Cost/share'])/da['Cost/share'])*100,2)
da['total_G/l']=round((da['Prices']*da['Quantity'])-(da['Cost Basis']),0)
da['Account_symbol'] = str(da['Account'])
da['Account_symbol'] = da.agg(lambda x: f"{x['Symbol']} - {x['Account']}", axis=1)
da = da.sort_values(by=['total_G/l'],ascending=True)
da.index = da['Account_symbol']
da['positive'] = da['total_G/l'] > 0
del da.index.name
return(da)
def unrealized_profit_loss_graph(profit_loss):
# graph the profit and loss
#fig, (ax1,ax2) = plt.subplots(1,2,sharex=False,sharey=True,figsize=(16,8));
#fig, ax1 = plt.subplots(1,1,figsize=(16,8));
plt.style.use('ggplot');
fig = plt.figure()
ax1 = fig.add_subplot(111);
ax1.set_title('Total G/L (UNREALIZED - IN THE MARKET)');
#ax1 = fig.add_subplot() # Create matplotlib axes
ax2 = ax1.twiny()
profit_loss['total_G/l'].plot(kind='barh',color=profit_loss.positive.map({True: 'g', False: 'r'}))
profit_loss['gain_loss_perc'].plot(kind='barh',color=profit_loss.positive.map({True: 'b', False: 'y'}))
ax1.set_xlabel('%', fontsize=12);
ax2.set_xlabel('$', fontsize=12);
plt.xlim(-5000, 20000);
plt.xticks(rotation=45);
align_xaxis(ax1,0,ax2,0);
plt.show();
# Profit and loss
profit_loss = unrealized_profit_loss()
p_l = unrealized_profit_loss_graph(profit_loss)
xls file I read from
You failed to provide a working example. Nevertheless, try the following: Pass the respective axis to the plot function and then try aligning
def unrealized_profit_loss_graph(profit_loss):
plt.style.use('ggplot')
fig = plt.figure()
ax1 = fig.add_subplot(111)
profit_loss['total_G/l'].plot(kind='barh',
color=profit_loss.positive.map({True: 'g', False: 'r'}),
ax=ax1)
ax2 = ax1.twiny()
profit_loss['gain_loss_perc'].plot(kind='barh',
color=profit_loss.positive.map({True: 'b', False: 'y'}),
ax=ax2)
ax1.set_xlabel('%', fontsize=12)
ax2.set_xlabel('$', fontsize=12)
plt.xlim(-5000, 20000)
plt.xticks(rotation=45)
align_xaxis(ax1,0,ax2,0)
plt.show();
I want to remove the (ticks) axis with numbers to the right of the colorbar. I am using matplotlib with python as follows:
f = plt.figure()
ax = f.add_subplot(1,1,1)
i = ax.imshow(mat, cmap= 'gray')
cbar = f.colorbar(i)
If you just want to remove the ticks but keep the ticklabels, you can set the size of the ticks to be 0 as following
f = plt.figure()
ax = f.add_subplot(1,1,1)
mat = np.arange(100).reshape((10, 10))
i = ax.imshow(mat, cmap= 'viridis')
cbar = f.colorbar(i)
cbar.ax.tick_params(size=0)
If you want to remove both, the ticks and the labels, you can use set_ticks([]) by passing an empty list.
cbar.set_ticks([])
Another option is to provided a formatter or locator. Here two combinations of:
a formatter which sets any value to an empty sting ('')
a locator that doesn't place a tick.
See the official matplotlib docs for more formatters or locators.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
fig, ax = plt.subplots(ncols=1)
mat = np.arange(100).reshape((10, 10))
cs = ax.imshow(mat, cmap= 'viridis')
divider = make_axes_locatable(ax)
dvider_kwargs = dict(position="right", size="15%", pad=0.5)
fig.colorbar(cs,
cax=divider.append_axes(**dvider_kwargs),
format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''),
ticks = matplotlib.ticker.FixedLocator([]))
fig.colorbar(cs,
cax=divider.append_axes(**dvider_kwargs),
format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''))
fig.colorbar(cs,
cax=divider.append_axes(**dvider_kwargs))
plt.tight_layout()
With make_axes_locatable and cax=divider.append_axes the colorbars have all the same size.
Another example
# gen data
n = 100000
bins = np.arange(-10, 10, .1)
value = np.random.normal(loc=20.0, scale=10.0, size=n)
samples0 = np.random.multivariate_normal([-2, 0], [[1, 0], [0, 1]], n)
samples1 = np.random.multivariate_normal([4, 4], [[1, -.9], [-.9, 1]], n)
samples2 = np.random.multivariate_normal([4, -4], [[1, .6], [.6, 1]], n)
h0, e = np.histogramdd(samples0, bins=[bins, bins], density=True)
h1, e = np.histogramdd(samples1, bins=[bins, bins], density=True)
h2, e = np.histogramdd(samples2, bins=[bins, bins], density=True)
# create figure
fig, ax = plt.subplots(ncols=1, figsize=(3,2))
kwargs = dict(vmin=0, vmax=.3)
cs0 = plt.pcolormesh(e[0][:-1], e[1][:-1], np.ma.masked_equal(h0, 0), cmap='Blues', **kwargs)
cs1 = plt.pcolormesh(e[0][:-1], e[1][:-1], np.ma.masked_equal(h1, 0), cmap='Greens', **kwargs)
cs2 = plt.pcolormesh(e[0][:-1], e[1][:-1], np.ma.masked_equal(h2, 0), cmap='Reds', **kwargs)
# create colorbars
divider = make_axes_locatable(ax)
divider_kwargs = dict(position="right", size="5%", pad=0.1)
fig.colorbar(cs0, extend='max',
cax=divider.append_axes(**divider_kwargs),
format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''))
fig.colorbar(cs1, extend='max',
cax=divider.append_axes(**divider_kwargs),
format = matplotlib.ticker.FuncFormatter(lambda x, pos: ''))
fig.colorbar(cs2, extend='max',
cax=divider.append_axes(**divider_kwargs),
label='PDF')
# tune plot
ax.set_aspect('equal')
# ax.grid()
plt.tight_layout()
I'm scatter plotting values from pandas dataframe. I would like to annotate points only if the value is greater than 100. I have no idea how to go about it.
Here's the code I'm working with (it's terrible but I'm very new to this):
female_data = r'/home/jg/Desktop/hurricanedata_f.csv'
female_df = read_csv(female_data)
male_data = r'/home/jg/Desktop/hurricanedata_m.csv'
male_df = read_csv(male_data)
x = female_df['Year']
y = female_df['alldeaths']
z = female_df['Name']
y_mean = [np.mean(y) for i in x]
a = male_df['Year']
b = male_df['alldeaths']
b_mean = [np.mean(b) for i in b]
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.annotate('Agnes', xy=(1972,117))
ax1 = fig.add_subplot(1,1,1)
ax1.scatter(x,y, label = 'female', color = 'r')
ax2 = fig.add_subplot(1,1,1)
ax2.scatter(a,b, label = 'male')
ax3 = fig.add_subplot(1,1,1)
ax3.plot(x, y_mean, linestyle='--', color = 'r')
ax4 = fig.add_subplot(1,1,1)
ax4.plot(a, b_mean, linestyle='--', color = 'blue')
plt.title('Hurricanes')
plt.xlabel('Year')
plt.ylabel('Deaths')
plt.legend(loc='upper right')
plt.ylim([-5,300])
plt.xlim([1948,2020])
plt.show()
You can loop over all your data points and check if each is greater than 100. Then give those points an annotation.
import matplotlib.pyplot as plt
import numpy as np
import string
# Fake data
x = np.arange(10)
y = 10*np.random.rand(10) + 95
names = string.lowercase[:10] # first 10 lowercase letters
# Plot data
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x,y)
# Annonate points with y values greater than 100
for xi, yi, iname in zip(x,y,names): # Loop over x and y values
if yi > 100: # Check if y is greater than 100
ax.annotate(iname, (xi, yi),size = 30) # Add an annoatation.
plt.show()