I am trying to recreate this scatterplot with my own code: https://www.machinelearningplus.com/wp-content/uploads/2018/11/25_Distributed_Dotplot_Matplotlib-min.png
The code to produce this is:
(https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/#25.-Distributed-Dot-Plot)
# !pip install brewer2mpl
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import warnings; warnings.filterwarnings(action='once')
large = 22; med = 16; small = 12
params = {'axes.titlesize': large,
'legend.fontsize': med,
'figure.figsize': (16, 10),
'axes.labelsize': med,
'axes.titlesize': med,
'xtick.labelsize': med,
'ytick.labelsize': med,
'figure.titlesize': large}
plt.rcParams.update(params)
plt.style.use('seaborn-whitegrid')
sns.set_style("white")
%matplotlib inline
# Version
print(mpl.__version__) #> 3.0.0
print(sns.__version__) #> 0.9.0
import matplotlib.patches as mpatches
# Prepare Data
df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
cyl_colors = {4:'tab:red', 5:'tab:green', 6:'tab:blue', 8:'tab:orange'}
df_raw['cyl_color'] = df_raw.cyl.map(cyl_colors)
# Mean and Median city mileage by make
df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', ascending=False, inplace=True)
df.reset_index(inplace=True)
df_median = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.median())
# Draw horizontal lines
fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
ax.hlines(y=df.index, xmin=0, xmax=40, color='gray', alpha=0.5, linewidth=.5, linestyles='dashdot')
# Draw the Dots
for i, make in enumerate(df.manufacturer):
df_make = df_raw.loc[df_raw.manufacturer==make, :]
ax.scatter(y=np.repeat(i, df_make.shape[0]), x='cty', data=df_make, s=75, edgecolors='gray', c='w', alpha=0.5)
ax.scatter(y=i, x='cty', data=df_median.loc[df_median.index==make, :], s=75, c='firebrick')
# Annotate
ax.text(33, 13, "$red \; dots \; are \; the \: median$", fontdict={'size':12}, color='firebrick')
# Decorations
red_patch = plt.plot([],[], marker="o", ms=10, ls="", mec=None, color='firebrick', label="Median")
plt.legend(handles=red_patch)
ax.set_title('Distribution of City Mileage by Make', fontdict={'size':22})
ax.set_xlabel('Miles Per Gallon (City)', alpha=0.7)
ax.set_yticks(df.index)
ax.set_yticklabels(df.manufacturer.str.title(), fontdict={'horizontalalignment': 'right'}, alpha=0.7)
ax.set_xlim(1, 40)
plt.xticks(alpha=0.7)
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["bottom"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["left"].set_visible(False)
plt.grid(axis='both', alpha=.4, linewidth=.1)
plt.show()
However, whenever I try to run this code I get ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
So, I added .any() to y=np.repeat(i, df_make.shape[0]) and reran
And now i am getting ValueError: x and y must be the same size
Just from googling I understand that somehow the x and y are different dimensions, but I am not sure how to fix this and make it work so i can then apply it to my own data.
Thanks!
The error is probably caused by the fact that the first 'y' is an array and not a list.
ax.scatter(y=[i]*df_make.shape[0], ...)
full codeļ¼
import matplotlib.patches as mpatches
# Prepare Data
df_raw = pd.read_csv("https://github.com/selva86/datasets/raw/master/mpg_ggplot2.csv")
cyl_colors = {4:'tab:red', 5:'tab:green', 6:'tab:blue', 8:'tab:orange'}
df_raw['cyl_color'] = df_raw.cyl.map(cyl_colors)
# Mean and Median city mileage by make
df = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.mean())
df.sort_values('cty', ascending=False, inplace=True)
df.reset_index(inplace=True)
df_median = df_raw[['cty', 'manufacturer']].groupby('manufacturer').apply(lambda x: x.median())
# Draw horizontal lines
fig, ax = plt.subplots(figsize=(16,10), dpi= 80)
ax.hlines(y=df.index, xmin=0, xmax=40, color='gray', alpha=0.5, linewidth=.5, linestyles='dashdot')
# Draw the Dots
for i, make in enumerate(df.manufacturer):
df_make = df_raw.loc[df_raw.manufacturer==make, :]
ax.scatter(y=[i]*df_make.shape[0], x='cty', data=df_make, s=75, edgecolors='gray', c='w', alpha=0.5)
ax.scatter(y=i, x='cty', data=df_median.loc[df_median.index==make, :], s=75, c='firebrick')
# Annotate
ax.text(33, 13, "$red \; dots \; are \; the \: median$", fontdict={'size':12}, color='firebrick')
# Decorations
red_patch = plt.plot([],[], marker="o", ms=10, ls="", mec=None, color='firebrick', label="Median")
plt.legend(handles=red_patch)
ax.set_title('Distribution of City Mileage by Make', fontdict={'size':22})
ax.set_xlabel('Miles Per Gallon (City)', alpha=0.7)
ax.set_yticks(df.index)
ax.set_yticklabels(df.manufacturer.str.title(), fontdict={'horizontalalignment': 'right'}, alpha=0.7)
ax.set_xlim(1, 40)
plt.xticks(alpha=0.7)
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["bottom"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.gca().spines["left"].set_visible(False)
plt.grid(axis='both', alpha=.4, linewidth=.1)
plt.show()
Related
I try to produce a plot and want to automatically add text (in this case is percentage) to each circle in correspond to each y axis types. Any help would be very helpful.
# import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# Make some data
index=['Stream flow',
'Soil moisture',
'Water indices',
'Others',
'Temperature',
'Precipitation',
'Vegetative indices']
value=[2.13, 6.38, 10.64, 12.77, 17.73, 21.99, 28.37]
# create dataframe
percentages = pd.Series(value,index=index)
df = pd.DataFrame({'percentage' : percentages})
df = df.sort_values(by='percentage')
# we first need a numeric placeholder for the y axis
my_range=list(range(1,len(df.index)+1))
fig, ax = plt.subplots(figsize=(15,8))
# create for each expense type an horizontal line that starts at x = 0 with the length
plt.hlines(y=my_range, xmin=0, xmax=df['percentage']-0.5, color='black', alpha=0.8, linewidth=1)
# create for each expense type a dot at the level of the expense percentage value
line=plt.plot(df['percentage'], my_range, "o", markersize=30, color='#fd8c00', alpha=0.6, linewidth=0.3)
# set labels
ax.set_xlabel('Percentage', fontsize=15)
ax.set_ylabel('')
# set axis
ax.tick_params(axis='both', which='major', labelsize=14)
plt.yticks(my_range, df.index)
ax.set_xlim(0,30)
You can use matplotlib.axes.Axes.text:
x_space = 0.4
y_space = 0.05
fontsize = 7
for y_i, val in enumerate(value, 1):
ax.text(x = val - x_space, y = y_i - y_space, s = f'{val}%', fontsize = fontsize)
You have to adjust x_space, y_space and fontsize in order to fit properly the text within the circles.
Complete code
# import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# Make some data
index=['Stream flow',
'Soil moisture',
'Water indices',
'Others',
'Temperature',
'Precipitation',
'Vegetative indices']
value=[2.13, 6.38, 10.64, 12.77, 17.73, 21.99, 28.37]
# create dataframe
percentages = pd.Series(value,index=index)
df = pd.DataFrame({'percentage' : percentages})
df = df.sort_values(by='percentage')
# we first need a numeric placeholder for the y axis
my_range=list(range(1,len(df.index)+1))
fig, ax = plt.subplots(figsize=(15,8))
# create for each expense type an horizontal line that starts at x = 0 with the length
plt.hlines(y=my_range, xmin=0, xmax=df['percentage']-0.5, color='black', alpha=0.8, linewidth=1)
# create for each expense type a dot at the level of the expense percentage value
line=plt.plot(df['percentage'], my_range, "o", markersize=30, color='#fd8c00', alpha=0.6, linewidth=0.3)
# set labels
ax.set_xlabel('Percentage', fontsize=15)
ax.set_ylabel('')
# set axis
ax.tick_params(axis='both', which='major', labelsize=14)
plt.yticks(my_range, df.index)
ax.set_xlim(0,30)
x_space = 0.4
y_space = 0.05
for y_i, val in enumerate(value, 1):
ax.text(x = val - x_space, y = y_i - y_space, s = f'{val:>5.2f}%', fontsize = 7)
plt.show()
Same code as above, but with increased circle radius and font, in order to improve readability.
# import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# Make some data
index=['Stream flow',
'Soil moisture',
'Water indices',
'Others',
'Temperature',
'Precipitation',
'Vegetative indices']
value=[2.13, 6.38, 10.64, 12.77, 17.73, 21.99, 28.37]
# create dataframe
percentages = pd.Series(value,index=index)
df = pd.DataFrame({'percentage' : percentages})
df = df.sort_values(by='percentage')
# we first need a numeric placeholder for the y axis
my_range=list(range(1,len(df.index)+1))
fig, ax = plt.subplots(figsize=(15,8))
# create for each expense type an horizontal line that starts at x = 0 with the length
plt.hlines(y=my_range, xmin=0, xmax=df['percentage']-0.85, color='black', alpha=0.8, linewidth=1)
# create for each expense type a dot at the level of the expense percentage value
line=plt.plot(df['percentage'], my_range, "o", markersize=50, color='#fd8c00', alpha=0.6, linewidth=0.3)
# set labels
ax.set_xlabel('Percentage', fontsize=15)
ax.set_ylabel('')
# set axis
ax.tick_params(axis='both', which='major', labelsize=14)
plt.yticks(my_range, df.index)
ax.set_xlim(0,30)
ax.set_ylim(0, len(value) + 1)
x_space = 0.75
y_space = 0.06
fontsize = 12
for y_i, val in enumerate(value, 1):
ax.text(x = val - x_space, y = y_i - y_space, s = f'{val:>5.2f}%', fontsize = fontsize)
plt.show()
Even better, you can use matplotlib.axes.Axes.annotate to get rid of x_space and y_space:
fontsize = 12
for y_i, x_i in enumerate(value, 1):
ax.annotate(f'{x_i:>5.2f}%', xy = (x_i, y_i), xytext = (0, 0), textcoords = 'offset points', ha = 'center', va = 'center', fontsize = fontsize)
You still have to adjust the fontsize to properly fit the radius of the circles.
I am doing a horizontal bar chart but struggling with adjusting ylim, or maybe another parameter to make my labels clearer and make all the labels fit the y axis . I played around with ylim and the text size can be bigger or smaller but the bars do not fit the y axis. Any idea about the right approach?
My code:
import matplotlib.pyplot as plt #we load the library that contains the plotting capabilities
from operator import itemgetter
D=[]
for att, befor, after in zip(df_portion['attributes'], df_portion['2005_2011 (%)'], df_portion['2012_2015 (%)']):
i=(att, befor, after)
D.append(i)
Dsort = sorted(D, key=itemgetter(1), reverse=False) #sort the list in order of usage
attri = [x[0] for x in Dsort]
aft = [x[1] for x in Dsort]
bef = [x[2] for x in Dsort]
ind = np.arange(len(attri))
width=3
ax = plt.subplot(111)
ax.barh(ind, aft, width,align='center',alpha=1, color='r', label='from 2012 to 2015') #a horizontal bar chart (use .bar instead of .barh for vertical)
ax.barh(ind - width, bef, width, align='center', alpha=1, color='b', label='from 2005 to 2008') #a horizontal bar chart (use .bar instead of .barh for vertical)
ax.set(yticks=ind, yticklabels=attri,ylim=[1, len(attri)/2])
plt.xlabel('Frequency distribution (%)')
plt.title('Frequency distribution (%) of common attributes between 2005_2008 and between 2012_2015')
plt.legend()
plt.show()
This is the plot for above code
To make the labels fit, you need to set a smaller fontsize, or use a larger figsize. Changing the ylim will either just show a subset of the bars (in case ylim is set too narrow), or will show more whitespace (when ylim is larger).
The biggest problem in the code is width being too large. Twice the width needs to fit over a distance of 1.0 (the ticks are placed via ind, which is an array 0,1,2,...). As matplotlib calls the thickness of a horizontal bar plot "height", this name is used in the example code below. Using align='edge' lets you position the bars directly (align='center' will move them half their "height").
Pandas has simple functions to sort dataframes according to one or more rows.
Code to illustrate the ideas:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# first create some test data
df = pd.DataFrame({'attributes': ["alpha", "beta", "gamma", "delta", "epsilon", "zata", "eta", "theta", "iota",
"kappa", "lambda", "mu", "nu", "xi", "omikron", "pi", "rho", "sigma", "tau",
"upsilon", "phi", "chi", "psi", "omega"]})
totals_2005_2011 = np.random.uniform(100, 10000, len(df))
totals_2012_2015 = totals_2005_2011 * np.random.uniform(0.70, 2, len(df))
df['2005_2011 (%)'] = totals_2005_2011 / totals_2005_2011.sum() * 100
df['2012_2015 (%)'] = totals_2012_2015 / totals_2012_2015.sum() * 100
# sort all rows via the '2005_2011 (%)' column, sort from large to small
df = df.sort_values('2005_2011 (%)', ascending=False)
ind = np.arange(len(df))
height = 0.3 # two times height needs to be at most 1
fig, ax = plt.subplots(figsize=(12, 6))
ax.barh(ind, df['2012_2015 (%)'], height, align='edge', alpha=1, color='crimson', label='from 2012 to 2015')
ax.barh(ind - height, df['2005_2011 (%)'], height, align='edge', alpha=1, color='dodgerblue', label='from 2005 to 2011')
ax.set_yticks(ind)
ax.set_yticklabels(df['attributes'], fontsize=10)
ax.grid(axis='x')
ax.set_xlabel('Frequency distribution (%)')
ax.set_title('Frequency distribution (%) of common attributes between 2005_2011 and between 2012_2015')
ax.legend()
ax.margins(y=0.01) # use smaller margins in the y-direction
plt.tight_layout()
plt.show()
The seaborn library has some functions to create barplots with multiple bars per attribute, without the need to manually fiddle with bar positions. Seaborn prefers its data in "long form", which can be created via pandas' melt().
Example code:
import seaborn as sns
df = df.sort_values('2005_2011 (%)', ascending=True)
df_long = df.melt(id_vars='attributes', value_vars=['2005_2011 (%)', '2012_2015 (%)'],
var_name='period', value_name='distribution')
fig, ax = plt.subplots(figsize=(12, 6))
sns.barplot(data=df_long, y='attributes', x='distribution', hue='period', palette='turbo', ax=ax)
ax.set_xlabel('Frequency distribution (%)')
ax.set_title('Frequency distribution (%) of common attributes between 2005_2011 and between 2012_2015')
ax.grid(axis='x')
ax.tick_params(axis='y', labelsize=12)
sns.despine()
plt.tight_layout()
plt.show()
I have this piece of code that compares chess openings to their outcomes:
z = df2.groupby(["winner", "opening_name"]).size().unstack().fillna(0).astype(int)
fig, ax = plt.subplots(figsize=(32, 16))
sns.heatmap(z.apply(lambda x: x/x.sum()), xticklabels=True, yticklabels=True, cmap='YlOrBr',
annot=True, linewidths=0.005, linecolor='black', annot_kws={"fontsize":16}, fmt='.2f', cbar=False)
plt.xticks(fontsize = 16)
plt.yticks(fontsize=16)
plt.show()
del z
This is the result:
Is there a way to change seaborn's sns.heatmap's configurations so that it applies the color scaling horizontally instead of vertically? Without changing the given values?
If I change the method's axis(z.apply(lambda x: x/x.sum(), axis = 1), it also changes the actual outcomes:
I want to apply the horizontal color scaling of the 2nd picture to the data of the first picture.
Instead of just setting annot=True, it can also be an array of values (or o labels). You can use a different data= parameter to define the coloring.
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
np.random.seed(31416)
weights = np.random.rand(20) ** 1.5
weights /= weights.sum() # random weights summing to 1
N = 2000
df2 = pd.DataFrame({'winner': np.random.choice(['white', 'draw', 'black'], N, p=[0.47, 0.07, 0.46]),
'opening_name': np.random.choice([*'ABCDEFGHIJKLMNOPQRST'], N, p=weights)})
z = df2.groupby(["winner", "opening_name"]).size().unstack().fillna(0).astype(int)
fig, ax = plt.subplots(figsize=(32, 16))
sns.heatmap(data=z.apply(lambda x: x / x.sum(), axis=1),
annot=z.apply(lambda x: x / x.sum()),
xticklabels=True, yticklabels=True, cmap='YlOrBr',
linewidths=0.005, linecolor='black', annot_kws={"fontsize": 16}, fmt='.2f', cbar=False, ax=ax)
ax.tick_params(labelsize=16)
plt.tight_layout()
plt.show()
I have a ecdf plot like this:
penguins = sns.load_dataset("penguins")
fig, ax = plt.subplots(figsize = (10,8))
sns.ecdfplot(data=penguins, x="bill_length_mm", hue="species")
ax.axhline(.25, linestyle = '--', color ='#cfcfcf', lw = 2, alpha = 0.75)
how to find the x values on this intersecting axhline?
You could loop through the generated curves (ax.get_lines()), extract their coordinates and search for the index of the first y-value larger than the desired y-value.
Here is some illustrating code (note that sns.ecdfplot() should get ax as parameter):
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
penguins = sns.load_dataset("penguins")
fig, ax = plt.subplots(figsize=(10, 8))
sns.ecdfplot(data=penguins, x="bill_length_mm", hue="species", ax=ax)
y_special = 0.25
for line in ax.get_lines():
x, y = line.get_data()
ind = np.argwhere(y >= y_special)[0, 0] # first index where y is larger than y_special
# x[ind] is the desired x-value
ax.text(x[ind], y_special, f' {x[ind]:.1f}', ha='left', va='top') # maybe color=line.get_color()
ax.axhline(y_special, linestyle='--', color='#cfcfcf', lw=2, alpha=0.75)
plt.show()
PS: Optionally you could add these x-values to the legend:
for line, legend_text in zip(ax.get_lines(), ax.legend_.get_texts()):
x, y = line.get_data()
ind = np.argwhere(y >= y_special)[0, 0]
legend_text.set_text(f'{x[ind]:5.2f} {legend_text.get_text()}')
This is a case where it's better to use the computational tools that pandas provides instead of trying to back quantitative values out from a visual representation.
If you want the values corresponding to the .25 quantile for each species, you should do:
penguins.groupby("species")["bill_length_mm"].quantile(.25)
which returns
species
Adelie 36.75
Chinstrap 46.35
Gentoo 45.30
Name: bill_length_mm, dtype: float64
I'm triying to make a figure where the stem plot has the baseline on the data of dataframe_3_merged['TOTAL'].
import numpy as np
from eurostatapiclient import EurostatAPIClient
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import seaborn as sns
import pandas as pd
#Set versions and formats, so far only the ones used here are availeable and call client
VERSION = 'v2.1'
FORMAT = 'json'
LANGUAGE = 'en'
client = EurostatAPIClient(VERSION, FORMAT, LANGUAGE)
dataframe_3_query_total = 'ilc_peps01?precision=1&sex=T&geo=AT&geo=BE&geo=BG&geo=CH&geo=CY&geo=CZ&geo=DK&geo=EA19&geo=EE&geo=EL&geo=ES&geo=EU28&geo=FI&geo=FR&geo=HR&geo=HU&geo=IE&geo=IS&geo=IT&geo=LT&geo=LU&geo=LV&geo=ME&geo=MK&geo=MT&geo=NL&geo=NO&geo=PL&geo=PT&geo=RO&geo=RS&geo=SE&geo=SI&geo=SK&geo=TR&geo=UK&unit=PC&unitLabel=label&time=2018&age=TOTAL'
dataframe_3_query_urb = 'ilc_peps13?precision=1°_urb=DEG1°_urb=DEG2°_urb=DEG3&geo=AT&geo=BE&geo=BG&geo=CH&geo=CY&geo=CZ&geo=DE&geo=DK&geo=EA19&geo=EE&geo=EL&geo=ES&geo=EU28&geo=FI&geo=FR&geo=HR&geo=HU&geo=IE&geo=IS&geo=IT&geo=LT&geo=LU&geo=LV&geo=MK&geo=MT&geo=NL&geo=NO&geo=PL&geo=PT&geo=RO&geo=RS&geo=SE&geo=SI&geo=SK&geo=UK&unit=PC&unitLabel=label&time=2018'
dataframe_3_total = client.get_dataset(dataframe_3_query_total).to_dataframe().pivot(index = 'geo',columns = 'age',values = 'values')
dataframe_3_urb =client.get_dataset(dataframe_3_query_urb).to_dataframe().pivot(index = 'geo',columns = 'deg_urb',values = 'values')
dataframe_3_merged = dataframe_3_total.join(dataframe_3_urb).dropna()
fig, ax = plt.subplots(figsize=(15, 4))
plt.ylim(0,51)
x = range(0,32,1)
stem_1 =plt.stem(x,dataframe_3_merged['DEG1'])
stem_2=plt.stem(x, dataframe_3_merged['DEG2'])
stem_3=plt.stem(x, dataframe_3_merged['DEG3'])
plt.setp(stem_2, color = 'r')
plt.setp(stem_3, color = 'g')
scatterplot= sns.scatterplot(x=dataframe_3_merged.index, #We draw the scatterplot and specify the arguments
y = dataframe_3_merged['TOTAL'],
ax=ax ,
s = 100 ,
legend = False,
marker="_",
color = 'b')
The goal is to have a plot similar to this image:
I tried to use the list dataframe_3_merged['TOTAL'] as the parameter in the bottom argument of plt.stem but I have this traceback: ValueError: setting an array element with a sequence.
Thank you for your help!
You could replace each stem plot by a scatter plot and a plot of vertical lines (plt.vlines). Setting the zorder=0 ensures the lines are drawn behind the dots.
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
names = ['hydrogen', 'helium', 'lithium', 'beryllium', 'boron', 'carbon', 'nitrogen', 'oxygen', 'fluorine', 'neon', 'sodium', 'magnesium', 'aluminium', 'silicon', 'phosphorus', 'sulphur', 'chlorine', 'argon', 'potassium', 'calcium', 'scandium', 'titanium', 'vanadium', 'chromium', 'manganese', 'iron', 'cobalt', 'nickel', 'copper', 'zinc', 'gallium', 'germanium', 'arsenic', 'selenium', 'bromine', 'krypton']
N = len(names)
df = pd.DataFrame({'Deg1': 35 + np.random.normal(size=N).cumsum(),
'Deg2': 25 + np.random.normal(size=N).cumsum(),
'Deg3': 15 + np.random.normal(size=N).cumsum()},
index=names)
df['Total'] = df.mean(axis=1)
for deg, color, label in zip(['Deg1', 'Deg2', 'Deg3'], ['tomato', 'orange', 'palegreen'],
['label1', 'label2', 'label3']):
plt.vlines(df.index, df[deg], df['Total'], lw=0.2, color='k', zorder=0)
plt.scatter(df.index, df[deg], marker='o', color=color, label=label)
plt.scatter(df.index, df['Total'], marker='_', color='deepskyblue', s=100)
plt.xticks(rotation='vertical')
plt.ylim(0, 51)
plt.margins(x=0.02)
plt.legend(ncol=3, bbox_to_anchor=(0.5, -0.4), loc='upper center')
plt.grid(True, axis='y')
plt.tick_params(length=0)
for where in ['top', 'left', 'right']:
plt.gca().spines[where].set_visible(False)
plt.tight_layout()
plt.show()