How to create a scatter plot or heatmap of spearman's correlation
Question:
I have two dataframes ‘A’ and ‘B’ each of them is having 1000 values(some values are missing from each column).
Dataframe ‘A’
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Dataframe ‘B’
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I want to compute spearman’s correlation of the dataframes and want to plot them. Plot can be Scatter or heatmap.
But i don’t have any idea how it can be done.
Any resource or reference will be helpful.
Thanks.
Answers:
Check this code:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
A = [...] # insert here your list of values for A
B = [...] # insert here your list of values for B
df = pd.DataFrame({'A': A,
'B': B})
corr = df.corr(method = 'spearman')
sns.heatmap(corr, annot = True)
plt.show()
I get this correlation matrix:
The column A is highly correlated with itself (obviously, this always happens), while the correlation between column A and B is very low.
Version info:
Python 3.7.0
matplotlib 3.2.1
pandas 1.0.4
seaborn 0.10.1
You can use the jointplot function from seaborn to plot the datapoints. This not only provides you with the scatterplot, but also with the marginal distribution.
df = pd.DataFrame({"A": A, "B": B})
sbn.jointplot(x="A", y="B", data=df, alpha=0.2)
To get the spearman’s correlation coefficient, you can use the spearmanr function from the scipy module:
from scipy.stats import spearmanr
r, p = spearmanr(df["A"], df["B"])
# r = 0.008703025102683665
I have two dataframes ‘A’ and ‘B’ each of them is having 1000 values(some values are missing from each column).
Dataframe ‘A’
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-1.61495353e-03, 3.72062420e-02, 1.66557311e-02, 2.39392450e-01,
-3.91891332e-02, 3.94344811e-02, 4.10956733e-02, 6.69258037e-02,
7.92391216e-02, 2.59883593e+00, 1.54048404e+00, -4.92893250e-01,
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2.05908855e+00, -2.36161146e-01, -6.62032149e-01, -3.46654905e-01,
1.60181172e+00, 1.65443393e+00, -3.77934113e-03, -7.94313157e-01,
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6100]
I want to compute spearman’s correlation of the dataframes and want to plot them. Plot can be Scatter or heatmap.
But i don’t have any idea how it can be done.
Any resource or reference will be helpful.
Thanks.
Check this code:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
A = [...] # insert here your list of values for A
B = [...] # insert here your list of values for B
df = pd.DataFrame({'A': A,
'B': B})
corr = df.corr(method = 'spearman')
sns.heatmap(corr, annot = True)
plt.show()
I get this correlation matrix:
The column A is highly correlated with itself (obviously, this always happens), while the correlation between column A and B is very low.
Version info:
Python 3.7.0
matplotlib 3.2.1
pandas 1.0.4
seaborn 0.10.1
You can use the jointplot function from seaborn to plot the datapoints. This not only provides you with the scatterplot, but also with the marginal distribution.
df = pd.DataFrame({"A": A, "B": B})
sbn.jointplot(x="A", y="B", data=df, alpha=0.2)
To get the spearman’s correlation coefficient, you can use the spearmanr function from the scipy module:
from scipy.stats import spearmanr
r, p = spearmanr(df["A"], df["B"])
# r = 0.008703025102683665