I am beginner in python and pandas
I have three CSV data. I want to make one histogram from these three dataframe.
I used this code
import pandas as pd
from matplotlib import pyplot as plt
X = pd.read_csv("data1.csv")
Y = pd.read_csv("data2.csv")
Z = pd.read_csv("data3.csv")
X.hist(column='speed', weights=X.ID,figsize=(20,10), stacked=True, bins=50, color = 'Blue', )
Y.hist(column='speed', weights=Y.ID,figsize=(20,10), stacked=True, bins=50, color = 'Red')
Z.hist(column='speed', weights=Z.ID,figsize=(20,10), stacked=True, bins=50, color = 'Grey')
plt.rc('xtick',labelsize=25)
plt.rc('ytick',labelsize=25)
but I got three different histograms.
How to make these three into one histogram with three colour of each histogram included?
I would go for this:
import pandas as pd
import matplotlib.pyplot as plt
X = pd.read_csv("data1.csv")
Y = pd.read_csv("data2.csv")
Z = pd.read_csv("data3.csv")
plt.hist([X.speed.values.flatten(), Y.speed.values.flatten(), Z.speed.values.flatten()], weights=[X.ID.values.flatten(), Y.ID.values.flatten(), Z.values.flatten()], label=['X', 'Y', 'Z'])
plt.legend()
plt.rc('xtick', labelsize=25)
plt.rc('ytick', labelsize=25)
Merge your dataframes:
X = pd.read_csv("data1.csv")
Y = pd.read_csv("data2.csv")
Z = pd.read_csv("data3.csv")
df = X.merge(Y).merge(Z)
df.hist(...)
Related
I have a Dataframe with 6 rows of data and 4 columns. Is there any way to generate a gif scatterplot (y which are the 4 columns in different color versus x which are the index rows) plot in which in every frame of the gif, first data point of the Column 1 and its first respective row data is plotted in different color versus the shared x axis which are the indexes, at the same time, column 2, 3 and 4 first data points are plotted, and this goes progressively until the last 6th point is plotted for all of the columns? If a gif is not possible at all, is there any other way to generate at least movie so that I can include in my ppt slide? I appreciate any feedback you might have! The error I am getting is generating an empty plot and saying: TypeError: cannot unpack non-iterable AxesSubplot object. But I am not sure if this is preventing the result from the plotting.
This is a sample of my data and code effort:
import pandas as pd
import numpy as np
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import random
from itertools import count
from IPython import display
row_data = np.arange(0, 6)
column_X = np.random.rand(6,)
column_Y = np.random.rand(6,)
column_Z = np.random.rand(6,)
column_K = np.random.rand(6,)
my_df = pd.DataFrame()
my_df['column_X'] = column_X
my_df['column_Y'] = column_Y
my_df['column_Z'] = column_Z
my_df['column_K'] = column_K
my_df.index = row_data
my_df['index'] = row_data
def animate(j):
fig, ax = plt.subplot(sharex= True)
ax[1]=my_df['column_X', color = 'blue']
ax[2]=my_df['column_Y', color = 'red']
ax[3]=my_df['column_Z', color = 'brown']
ax[4]=my_df['column_K', color = 'green']
y=my_df['index']
x.append()
y.append()
plt.xlabel(color = 'blue')
plt.ylabel(color = 'red')
ax.set_ylabel("progressive sales through time")
ax.set_xlabel("progressive time")
plt.plot(x,y)
animation_1 = animation.FuncAnimation(plt.gcf(),animate,interval=1000)
plt.show()
# Inside Jupyter:
video_1 = animation_1.to_html5_video()
html_code_1 = display.HTML(video_1)
display.display(html_code_1)
plt.tight_layout()
plt.show()
Good question! matplotlib animations can be tricky. I struggled a bit with this one, mainly because you want different colors for the different columns. You need 4 different Line2D objects to do this.
# VSCode notebook magic
%matplotlib widget
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
my_df = pd.DataFrame()
my_df["column_X"] = np.random.rand(6)
my_df["column_Y"] = np.random.rand(6)
my_df["column_Z"] = np.random.rand(6)
my_df["column_K"] = np.random.rand(6)
fig, ax = plt.subplots()
# four y-data lists, x-data is shared
xdata, y1, y2, y3, y4 = [], [], [], [], []
# four Line3D objects with different colors
graph1, = ax.plot([], [], 'ro-')
graph2, = ax.plot([], [], 'go-')
graph3, = ax.plot([], [], 'bo-')
graph4, = ax.plot([], [], 'ko-')
# set up the plot
plt.xlim(-1, 6)
plt.xlabel('Time')
plt.ylim(0, 1)
plt.ylabel('Price')
# animation function
def animate(i):
xdata.append(i)
y1.append(my_df.iloc[i,0])
y2.append(my_df.iloc[i,1])
y3.append(my_df.iloc[i,2])
y4.append(my_df.iloc[i,3])
graph1.set_data(xdata, y1)
graph2.set_data(xdata, y2)
graph3.set_data(xdata, y3)
graph4.set_data(xdata, y4)
return (graph1,graph2,graph3,graph4,)
anim = animation.FuncAnimation(fig, animate, frames=6, interval=500, blit=True)
anim.save('test.mp4')
#plt.show()
Here's the resulting .gif (converted from .mp4 using Adobe Express):
I managed to get a boxplot of 2 categories in the x-axis and a continuous variable in the y-axis. I just want to add to the plot the value of the quartiles, near to the boxes.
Like this:
Here is an example:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
np.random.seed(0)
df = pd.DataFrame({'churn': np.random.choice(['No', 'Si'], size=1000),
'value': np.random.random(size=1000)})
box_width = 0.5
ax = sns.boxplot(data=df, x='churn', y='value', width=box_width)
i = 0
for name, group in df.groupby('churn'):
Q1, Q3 = group['value'].quantile([0.25,0.75])
for q in (Q1, Q3):
x = i-box_width/2
y = q
ax.annotate('%.2f' % q, (x,y),
xytext=(x-0.1, y), textcoords='data',
arrowprops=dict(facecolor='black', shrink=0.05),
va='center', ha='right')
i+=1
How can the following code be modified to show the mean as well as the different error bars on each bar of the bar plot?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("white")
a,b,c,d = [],[],[],[]
for i in range(1,5):
np.random.seed(i)
a.append(np.random.uniform(35,55))
b.append(np.random.uniform(40,70))
c.append(np.random.uniform(63,85))
d.append(np.random.uniform(59,80))
data_df =pd.DataFrame({'stages':[1,2,3,4],'S1':a,'S2':b,'S3':c,'S4':d})
print("Delay:")
display(data_df)
S1 S2 S3 S4
0 43.340440 61.609735 63.002516 65.348984
1 43.719898 40.777787 75.092575 68.141770
2 46.015958 61.244435 69.399904 69.727380
3 54.340597 56.416967 84.399056 74.011136
meansd_df=data_df.describe().loc[['mean', 'std'],:].drop('stages', axis = 1)
display(meansd_df)
sns.set()
sns.set_style('darkgrid',{"axes.facecolor": ".92"}) # (1)
sns.set_context('notebook')
fig, ax = plt.subplots(figsize = (8,6))
x = meansd_df.columns
y = meansd_df.loc['mean',:]
yerr = meansd_df.loc['std',:]
plt.xlabel("Time", size=14)
plt.ylim(-0.3, 100)
width = 0.45
for i, j,k in zip(x,y,yerr): # (2)
ax.bar(i,j, width, yerr = k, edgecolor = "black",
error_kw=dict(lw=1, capsize=8, capthick=1)) # (3)
ax.set(ylabel = 'Delay')
from matplotlib import ticker
ax.yaxis.set_major_locator(ticker.MultipleLocator(10))
plt.savefig("Over.png", dpi=300, bbox_inches='tight')
Given the example data, for a seaborn.barplot with capped error bars, data_df must be converted from a wide format, to a tidy (long) format, which can be accomplished with pandas.DataFrame.stack or pandas.DataFrame.melt
It is also important to keep in mind that a bar plot shows only the mean (or other estimator) value
Sample Data and DataFrame
.iloc[:, 1:] is used to skip the 'stages' column at column index 0.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# given data_df from the OP, select the columns except stage and reshape to long format
df = data_df.iloc[:, 1:].melt(var_name='set', value_name='val')
# display(df.head())
set val
0 S1 43.340440
1 S1 43.719898
2 S1 46.015958
3 S1 54.340597
4 S2 61.609735
Updated as of matplotlib v3.4.2
Use matplotlib.pyplot.bar_label
See How to add value labels on a bar chart for additional details and examples with .bar_label.
Some formatting can be done with the fmt parameter, but more sophisticated formatting should be done with the labels parameter, as show in How to add multiple annotations to a barplot.
Tested with seaborn v0.11.1, which is using matplotlib as the plot engine.
fig, ax = plt.subplots(figsize=(8, 6))
# add the plot
sns.barplot(x='set', y='val', data=df, capsize=0.2, ax=ax)
# add the annotation
ax.bar_label(ax.containers[-1], fmt='Mean:\n%.2f', label_type='center')
ax.set(ylabel='Mean Time')
plt.show()
plot with seaborn.barplot
Using matplotlib before version 3.4.2
The default for the estimator parameter is mean, so the height of the bar is the mean of the group.
The bar height is extracted from p with .get_height, which can be used to annotate the bar.
fig, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x='set', y='val', data=df, capsize=0.2, ax=ax)
# show the mean
for p in ax.patches:
h, w, x = p.get_height(), p.get_width(), p.get_x()
xy = (x + w / 2., h / 2)
text = f'Mean:\n{h:0.2f}'
ax.annotate(text=text, xy=xy, ha='center', va='center')
ax.set(xlabel='Delay', ylabel='Time')
plt.show()
Seaborn is most powerfull with long form data. So you might want to transform your data, something like this:
sns.barplot(data=data_df.melt('stages', value_name='Delay', var_name='Time'),
x='Time', y='Delay',
capsize=0.1, edgecolor='k')
Output:
I have a notebook with 2* bar charts, one is winter data & one is summer data. I have counted the total of all the crimes and plotted them in a bar chart, using code:
ax = summer["crime_type"].value_counts().plot(kind='bar')
plt.show()
Which shows a graph like:
I have another chart nearly identical, but for winter:
ax = winter["crime_type"].value_counts().plot(kind='bar')
plt.show()
And I would like to have these 2 charts compared against one another in the same bar chart (Where every crime on the x axis has 2 bars coming from it, one winter & one summer).
I have tried, which is just me experimenting:
bx = (summer["crime_type"],winter["crime_type"]).value_counts().plot(kind='bar')
plt.show()
Any advice would be appreciated!
The following generates dummies of your data and does the grouped bar chart you wanted:
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
s = "Crime Type Summer|Crime Type Winter".split("|")
# Generate dummy data into a dataframe
j = {x: [random.choice(["ASB", "Violence", "Theft", "Public Order", "Drugs"]
) for j in range(300)] for x in s}
df = pd.DataFrame(j)
index = np.arange(5)
bar_width = 0.35
fig, ax = plt.subplots()
summer = ax.bar(index, df["Crime Type Summer"].value_counts(), bar_width,
label="Summer")
winter = ax.bar(index+bar_width, df["Crime Type Winter"].value_counts(),
bar_width, label="Winter")
ax.set_xlabel('Category')
ax.set_ylabel('Incidence')
ax.set_title('Crime incidence by season, type')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(["ASB", "Violence", "Theft", "Public Order", "Drugs"])
ax.legend()
plt.show()
With this script I got:
You can check out the demo in the matplotlib docs here: https://matplotlib.org/gallery/statistics/barchart_demo.html
The important thing to note is the index!
index = np.arange(5) # Set an index of n crime types
...
summer = ax.bar(index, ...)
winter = ax.bar(index+bar_width, ...)
...
ax.set_xticks(index + bar_width / 2)
These are the lines that arrange the bars on the horizontal axis so that they are grouped together.
Create a pandas dataframe with 3 columns crimetype, count, Season and try this function.
#Importing required packages
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MaxNLocator
#Function Creation
def plt_grouped_bar(Plot_Nm,group_bar,x, y,plt_data,**bar_kwargs):
plt_fig=plt.figure(figsize=(18,9))
ax=plt_fig.add_subplot()
g = sns.catplot(x=x, y=y, hue=group_bar,data=plt_data,ax=ax,kind="bar",**bar_kwargs)
for p in ax.patches:
height = p.get_height()
ax.text(x = p.get_x()+(p.get_width()/2),
y = height+0.05,
s = '{:.0f}'.format(height),
ha = 'center',va = 'bottom',zorder=20, rotation=90)
ax.set_title(Plot_Nm,fontweight="bold",fontsize=18,alpha=0.7,y=1.03)
g.set_xticklabels(x,fontsize=10,alpha=0.8,fontweight="bold")
plt.setp(ax.get_xticklabels(), rotation=90)
ax.set_yticklabels("")
ax.set_xlabel("")
ax.set_ylabel("")
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.tick_params(axis=u'both',length=0)
ax.legend(loc='upper right')
for spine in ax.spines:
ax.spines[spine].set_visible(False)
plt.close()
#Calling the function
plt_grouped_bar('Title of bar','weather','crimetype','count',pandasdataframename)
I'm wondering how can I do the following:
I have a DataFrame with points and classes. I'd like to draw all points and use one color for each class. How can I specify how classes refer to colors in the legend?
fig = plt.figure(figsize=(18,10), dpi=1600)
df = pd.DataFrame(dict(points1 = data_plot[:,0], points2 = data_plot[:,1], \
target = target[0:2000]))
colors = {1: 'green', 2:'red', 3:'blue', 4:'yellow', 5:'orange', 6:'pink', \
7:'brown', 8:'black', 9:'white'}
fig, ax = plt.subplots()
ax.scatter(df['points1'], df['points2'], c = df['target'].apply(lambda x: colors[x]))
The easiest way to get your legend to have separate entries for each color (and therefore it's target value) is to create a separate plot object for each target value.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
x = np.random.rand(100)
y = np.random.rand(100)
target = np.random.randint(1,9, size=100)
df = pd.DataFrame(dict(points1=x, points2=y, target=target))
colors = {1: 'green', 2:'red', 3:'blue', 4:'yellow', 5:'orange', 6:'pink', \
7:'brown', 8:'black', 9:'white'}
fig, ax = plt.subplots()
for k,v in colors.items():
series = df[df['target'] == k]
scat = ax.scatter(series['points1'], series['points2'], c=v, label=k)
plt.legend()