# Calculate Correlation of DataFrame Features/Columns with Pandas

Checking for correlation, and quantifying correlation is one of the key steps during exploratory data analysis and forming hypotheses.

Pandas is one of the most widely used data manipulation libraries, and it makes calculating correlation coefficients between all numerical variables very straightforward - with a single method call.

For more detailed and in-depth guides to Spearman and Pearson correlations, read our *"Calculating Spearman's Rank Correlation Coefficient in Python with Pandas"* and *"Calculating Pearson Correlation Coefficient in Python with Numpy"*!

Let's load in a dataset from Scikit-Learn and pack it into a `DataFrame`

:

```
import pandas as pd
import numpy as np
from sklearn.datasets import fetch_california_housing
# Target column is under ch.target, the rest is under ch.data
ch = fetch_california_housing(as_frame=True)
df = pd.DataFrame(data=ch.data, columns=ch.feature_names)
df['MedHouseVal'] = ch.target
df.head()
```

It's loaded correctly!

### Get All Correlation Coefficients

Now, to get the correlations between all of the numerical features, we simply call `df.corr()`

(which defaults to Pearson Correlation):

```
df.corr()
```

The method call returns a `DataFrame`

with the correlations and the same columns:

Though, since a tabular format isn't really intuitive or readable - let's plot this as a heat map:

```
import seaborn as sns
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(df.corr(), ax=ax, annot=True)
```

### Get Correlation to Target Variable

Say we're interested in a single target variable and would like to see which features correlate with it. We'll calculate the correlations with `df.corr()`

and then subset the resulting `DataFrame`

to only include the target column:

```
corr = df.corr()[['MedHouseVal']]
sns.heatmap(corr, annot=True)
```

### Sort Correlation Coefficients

More often than not - you'll want to sort the values as well:

```
corr = df.corr()[['MedHouseVal']].sort_values(by='MedHouseVal', ascending=False)
sns.heatmap(corr, annot=True)
```

### Pearson, Spearman and Kendall Rank Coefficients with Pandas

The `corr()`

method accepts three coefficient methods - `'pearson'`

, `'spearman'`

and `'kendall'`

:

```
fig, ax = plt.subplots(1,3, figsize=(18, 8))
corr1 = df.corr('pearson')[['MedHouseVal']].sort_values(by='MedHouseVal', ascending=False)
corr2 = df.corr('spearman')[['MedHouseVal']].sort_values(by='MedHouseVal', ascending=False)
corr3 = df.corr('kendall')[['MedHouseVal']].sort_values(by='MedHouseVal', ascending=False)
sns.heatmap(corr1, ax=ax[0], annot=True)
sns.heatmap(corr2, ax=ax[1], annot=True)
sns.heatmap(corr3, ax=ax[2], annot=True)
```

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