reconstruction figure legend in pandas

ax = df.plot()

years = [2005, 2007, 2008, 2009, 2011, 2012]
# you can get years from you dataframe(but without seeing the dataframe I can 't say exactly how)
      # legend also accepts a Series or numpy array ax.legend(years, loc = 'best') plt.show()

DataFrame1.plot(legend = False)
patch, labels = ax.get_legend_handels_labels()
DateFrame1.legend(str(unique(DataFrame1['Year'].value)), loc = 'best')
plt.show()

ax = df.plot()

years = [2005, 2007, 2008, 2009, 2011, 2012]
# you can get years from you dataframe(but without seeing the dataframe I can 't say exactly how)
      # legend also accepts a Series or numpy array ax.legend(years, loc = 'best') plt.show()

DataFrame1.plot(legend = False)
patch, labels = ax.get_legend_handels_labels()
DateFrame1.legend(str(unique(DataFrame1['Year'].value)), loc = 'best')
plt.show()

import matplotlib.pyplot as plt
plt.style.use('classic')

% matplotlib inline
import numpy as np

x = np.linspace(0, 10, 1000)
fig, ax = plt.subplots()
ax.plot(x, np.sin(x), '-b', label = 'Sine')
ax.plot(x, np.cos(x), '--r', label = 'Cosine')
ax.axis('equal')
leg = ax.legend();

ax.legend(loc = 'upper left', frameon = False)
fig

ax.legend(frameon = False, loc = 'lower center', ncol = 2)
fig

ax.legend(fancybox = True, framealpha = 1, shadow = True, borderpad = 1)
fig

In[]:

   import pandas as pd

import pandas as pd

import pandas as pd

In[]:

   df_asthma = pd.read_csv("data/asthma.csv")

df_asthma = pd.read_csv("data/asthma.csv")

df_asthma = pd.read_csv("data/asthma.csv")

In[]:

   df_asthma.head()

df_asthma.head()

df_asthma.head()

import plotly.tools as tls
tls.embed('https://plotly.com/~chris/7365')

import pandas as pd
from sqlalchemy
import create_engine # database connection
import datetime as dt
from IPython.display
import display

import plotly.plotly as py # interactive graphing
from plotly.graph_objs
import Bar, Scatter, Marker, Layout

display(pd.read_csv('311_100M.csv', nrows = 2).head())
display(pd.read_csv('311_100M.csv', nrows = 2).tail())

!wc - l < 311_100 M.csv # Number of lines in dataset

 8281035

disk_engine = create_engine('sqlite:///311_8M.db') # Initializes database with filename 311_8 M.db in current directory

def compute_roc(y_true, y_pred, plot = False):
   ""
"
TODO
   : param y_true: ground truth: param y_pred: predictions: param plot:
   : return:
      ""
"
fpr, tpr, _ = roc_curve(y_true, y_pred)
auc_score = auc(fpr, tpr)
if plot:
   plt.figure(figsize = (7, 6))
plt.plot(fpr, tpr, color = 'blue',
   label = 'ROC (AUC = %0.4f)' % auc_score)
plt.legend(loc = 'lower right')
plt.title("ROC Curve")
plt.xlabel("FPR")
plt.ylabel("TPR")
plt.show()

return fpr, tpr, auc_score

def plot_wh_methods(): # from utils.utils
import * ;
plot_wh_methods()
# Compares the two methods
for width - height anchor multiplication
# https: //github.com/ultralytics/yolov3/issues/168
   x = np.arange(-4.0, 4.0, .1)
ya = np.exp(x)
yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2

fig = plt.figure(figsize = (6, 3), dpi = 150)
plt.plot(x, ya, '.-', label = 'yolo method')
plt.plot(x, yb ** 2, '.-', label = '^2 power method')
plt.plot(x, yb ** 2.5, '.-', label = '^2.5 power method')
plt.xlim(left = -4, right = 4)
plt.ylim(bottom = 0, top = 6)
plt.xlabel('input')
plt.ylabel('output')
plt.legend()
fig.tight_layout()
fig.savefig('comparison.png', dpi = 200)

def plot(PDF, figName, imgpath, show = False, save = True):
   # plot
output = PDF.get_constraint_value()
plt.plot(PDF.experimentalDistances, PDF.experimentalPDF, 'ro', label = "experimental", markersize = 7.5, markevery = 1)
plt.plot(PDF.shellsCenter, output["pdf"], 'k', linewidth = 3.0, markevery = 25, label = "total")

styleIndex = 0
for key in output:
   val = output[key]
if key in ("pdf_total", "pdf"):
   continue
elif "inter" in key:
   plt.plot(PDF.shellsCenter, val, STYLE[styleIndex], markevery = 5, label = key.split('rdf_inter_')[1])
styleIndex += 1
plt.legend(frameon = False, ncol = 1)
# set labels
plt.title("$\\chi^{2}=%.6f$" % PDF.squaredDeviations, size = 20)
plt.xlabel("$r (\AA)$", size = 20)
plt.ylabel("$g(r)$", size = 20)
# show plot
if save: plt.savefig(figName)
if show: plt.show()
plt.close()

def make_plot(files, labels):
   plt.figure()
for file_idx in range(len(files)):
   rot_err, trans_err = read_csv(files[file_idx])
success_dict = count_success(trans_err)

x_range = success_dict.keys()
x_range.sort()
success = []
for i in x_range:
   success.append(success_dict[i])
success = np.array(success) / total_cases

plt.plot(x_range, success, linewidth = 3, label = labels[file_idx])
# plt.scatter(x_range, success, s = 50)
plt.ylabel('Success Ratio', fontsize = 40)
plt.xlabel('Threshold for Translation Error', fontsize = 40)
plt.tick_params(labelsize = 40, width = 3, length = 10)
plt.grid(True)
plt.ylim(0, 1.005)
plt.yticks(np.arange(0, 1.2, 0.2))
plt.xticks(np.arange(0, 2.1, 0.2))
plt.xlim(0, 2)
plt.legend(fontsize = 30, loc = 4)

def show(mnist, targets, ret):
   target_ids = range(len(set(targets)))

colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k', 'violet', 'orange', 'purple']

plt.figure(figsize = (12, 10))

ax = plt.subplot(aspect = 'equal')
for label in set(targets):
   idx = np.where(np.array(targets) == label)[0]
plt.scatter(ret[idx, 0], ret[idx, 1], c = colors[label], label = label)

for i in range(0, len(targets), 250):
   img = (mnist[i][0] * 0.3081 + 0.1307).numpy()[0]
img = OffsetImage(img, cmap = plt.cm.gray_r, zoom = 0.5)
ax.add_artist(AnnotationBbox(img, ret[i]))

plt.legend()
plt.show()

def plot_mul(Y_hat, Y, pred_len):
   ""
"
PLots the predicted data versus true data

Input: Predicted data, True Data, Length of prediction
Output: return plot

Note: Run from timeSeriesPredict.py ""
"
fig = plt.figure(facecolor = 'white')
ax = fig.add_subplot(111)
ax.plot(Y, label = 'Y')
# Print the predictions in its respective series - length
for i, j in enumerate(Y_hat):
   shift = [None
      for p in range(i * pred_len)
   ]
plt.plot(shift + j, label = 'Y_hat')
plt.legend()
plt.show()