So I am trying to plot correlation Matrix (already calculated) in python. the table is like below:
And I would like it to look like this:
I am using the Following code in python:
import seaborn as sn
import matplotlib.pyplot as plt
import pandas as pd
data =pd.read_excel('/Desktop/wetchimp_global/corr/correlation_matrix.xlsx')
df = pd.DataFrame(data)
print (df)
corrMatrix = data.corr()
print (corrMatrix)
sn.heatmap(corrMatrix, annot=True)
plt.show()
Note that, the matrix is ready and I don't want to calculate the correlation again! but I failed to do that. Any suggestions?
You are recalculating the correlation with the following line:
corrMatrix = data.corr()
You then go on to utilize this recalculated variable in the heatmap here:
sn.heatmap(corrMatrix, annot=True)
plt.show()
To resolve this, instead of passing in the corrMatrix value which is the recalculated value, pass the pure excel data data or df (as df is just a copy of data). Thus, all the code you should need is:
import seaborn as sn
import matplotlib.pyplot as plt
import pandas as pd
data =pd.read_excel('/Desktop/wetchimp_global/corr/correlation_matrix.xlsx')
sn.heatmap(data, annot=True)
plt.show()
Note that this assumes, however, that your data IS ready for the heatmap as you suggest. As we online do not have access to your data we cannot confirm that.
I have deleted to frist column (names) and add them later so the code is as below:
import seaborn as sn
import matplotlib.pyplot as plt
import pandas as pd
data =pd.read_excel('/Users/yousefalbuhaisi/Desktop/wetchimp_global/corr/correlation_matrix.xlsx')
fig, ax = plt.subplots(dpi=150)
y_axis_labels = ['CLC','GIEMS','GLWD','LPX_BERN','LPJ_WSL','LPJ_WHyME','SDGVM','DLEM','ORCHIDEE','CLM4ME']
sn.heatmap(data,yticklabels=y_axis_labels, annot=True)
plt.show()
and the results are:
Related
I wrote the python code below to read a CSV and generate multiple plots at once. However, I can't figure out how to give matplotlib the Location value from the csv I'm reading as I would like to have each plot labeled with the location value that I am aggregating on. Please see the code and screenshot below.
import pandas as pd
import datetime
import csv
import matplotlib.pyplot as plt
from google.colab import files
import matplotlib.pyplot as plt
import numpy as np
myfile = files.upload()
df = pd.read_csv('Electricity.csv')
df2 = df.groupby(['Location'], as_index=False)
print(df2)
df2.plot(x='Period', y = 'score', ylim=(0,11))#IDK about ylim being necessary
plt.title('Location', color='black')
plt._show()
Plots
Get the axes retured by df2.plot, and since you're grouping by Location you'll have something similar to:
Location NaN
0 Location1 AxesSubplot(0.125,0.11;0.775x0.77)
1 Location2 AxesSubplot(0.125,0.11;0.775x0.77)
2 Location3 AxesSubplot(0.125,0.11;0.775x0.77)
Use the apply function with axis=1 to get current axis and the location. With the axis object use set_title using the Location for each plot.
...
df2.apply(print)
ax = df2.plot(x='Period', y='score', ylim=(0, 11))
ax.apply(lambda x: x[1].set_title(x['Location']), axis=1)
plt.show()
I've created a Brownian motion and then I have taken the last values of 1000 entries repeated 10000 times. I was able to plot the histogram using the following code as follows:
import seaborn as sns
import matplotlib.pyplot as plt
\\BM represents list of values generated by the Brownian motion
fig, (ax1,ax2) = plt.subplots(2)
ax1.hist(BM[:,-1],12)
I've been able to draw the KDE as follows, however i unable to merge the two diagrams together. Can someone please help me?
sns.kdeplot(data=BM[:,-1])
Try with sns.kdeplot(BM['col1']) where 'col1' is the name of the column you want to plot.
I'll give you a reproducible example that works for me.
import seaborn as sns
import pandas as pd
import numpy as np
BM = pd.DataFrame(np.array([-0.00871515, -0.0001227 , -0.01449098, 0.01808527, 0.00074193, 0.01145541]
, columns=['col1'])
BM.head(2)
col1
0 -0.008715
1 -0.000123
sns.kdeplot(BM['col1'])
Edit based on your additional question:
To have the histogram and a kde plot use this one:
sns.distplot(BM['col1'])
I am using the standard pandas.df.plot() function to plot two columns in a dataframe. For some reason, the x-axis values and the xlabel are not visible! There seem to be no options to turn them on in the function either (see https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html).
Does someone know what is going on, and how to correct it?
import matplotlib.cm as cm
import pandas as pd
ax1 = df.plot.scatter(x='t', y='hlReference', c='STEP_STRENGTH', cmap=cm.autumn);
gives this:
This is a bug with Jupyter notebooks displaying pandas scatterplots that have a colorscale displayed while using Matplotlib as the plotting backend.
#june-skeeter has a solution in the answers that works. Alternatively, pass sharex=False to df.plot.scatter and you don't need to create subplots.
import matplotlib.cm as cm
import pandas as pd
X = np.random.rand(10,3)
df = pd.DataFrame(X,columns=['t','hlReference', 'STEP_STRENGTH'])
df.plot.scatter(
x='t',
y='hlReference',
c='STEP_STRENGTH',
cmap=cm.autumn,
sharex=False
)
See discussion in this closed pandas issues. Which references the above solution in a related SO answer.
Still an issue with pandas v1.1.0. You can track the issue here: https://github.com/pandas-dev/pandas/issues/36064
Create your axes instance first and then send it as an argument to the plot()
import matplotlib.cm as cm
import pandas as pd
X = np.random.rand(10,3)
df = pd.DataFrame(X,columns=['t','hlReference', 'STEP_STRENGTH'])
fig,ax1=plt.subplots()
df.plot.scatter(x='t', y='hlReference', c='STEP_STRENGTH', cmap=cm.autumn,ax=ax1)
How can I achieve that using matplotlib?
Here is my code with the data you provided. As there's no class [they are all different, despite your first example in your question does have classes], I gave colors based on the numbers. You can definitely start alone from here, whatever result you want to achieve. You just need pandas, seaborn and matplotlib:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# import xls
df=pd.read_excel('data.xlsx')
# exclude Ranking values
df1 = df.ix[:,1:-1]
# for each element it takes the value of the xls cell
df2=df1.applymap(lambda x: float(x.split('\n')[1]))
# now plot it
df_heatmap = df2
fig, ax = plt.subplots(figsize=(15,15))
sns.heatmap(df_heatmap, square=True, ax=ax, annot=True, fmt="1.3f")
plt.yticks(rotation=0,fontsize=16);
plt.xticks(fontsize=12);
plt.tight_layout()
plt.savefig('dfcolorgraph.png')
Which produces the following picture.
I have a csv file which contains two columns where first column is fruit name and second column is count and I need to plot histogram using this csv as input to the code below. How do I make it possible. I just have to show first 20 entries where fruit names will be x axis and count will be y axis from entire csv file of 100 lines.
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('data.csv', header = None ,quoting=2)
data.hist(bins=10)
plt.xlim([0,100])
plt.ylim([50,500])
plt.title("Data")
plt.xlabel("fruits")
plt.ylabel("Frequency")
plt.show()
I edited the above program to plot a bar chart -
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('data.csv', sep=',',header=None)
data.values
print data
plt.bar(data[:,0], data[:,1], color='g')
plt.ylabel('Frequency')
plt.xlabel('Words')
plt.title('Title')
plt.show()
but this gives me an error 'Unhashable Type '. Can anyone help on this.
You can use the inbuilt plot of pandas, although you need to specify the first column is index,
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('data.csv', sep=',',header=None, index_col =0)
data.plot(kind='bar')
plt.ylabel('Frequency')
plt.xlabel('Words')
plt.title('Title')
plt.show()
If you need to use matplotlib, it may be easier to convert the array to a dictionary using data.to_dict() and extract the data to numpy array or something.