# Pandas - Data Correlations

## Finding Relationships

A great aspect of the Pandas module is the `corr()`

method.

The `corr()`

method calculates the relationship between each column in your data set.

The examples in this page uses a CSV file called: 'data.csv'.

Download data.csv. or Open data.csv

### Example

Show the relationship between the columns:

```
df.corr()
```

Try it Yourself »
### Result

Duration Pulse Maxpulse Calories Duration 1.000000 -0.155408 0.009403 0.922721 Pulse -0.155408 1.000000 0.786535 0.025120 Maxpulse 0.009403 0.786535 1.000000 0.203814 Calories 0.922721 0.025120 0.203814 1.000000

**Note:**
The `corr()`

method ignores "not numeric"
columns.

### Result Explained

The Result of the `corr()`

method is a table with a lot of numbers that represents
how well the relationship is between two columns.

The number varies from -1 to 1.

1 means that there is a 1 to 1 relationship (a perfect correlation), and for this data set, each time a value went up in the first column, the other one went up as well.

0.9 is also a good relationship, and if you increase one value, the other will probably increase as well.

-0.9 would be just as good relationship as 0.9, but if you increase one value, the other will probably go down.

0.2 means NOT a good relationship, meaning that if one value goes up does not mean that the other will.

**What is a good correlation?**
It depends on the use, but I think it is safe to say you have to have at least `0.6`

(or `-0.6`

) to call it a good correlation.

### Perfect Correlation:

We can see that "Duration" and "Duration" got the number `1.000000`

, which makes sense,
each column always has a perfect relationship with itself.

### Good Correlation:

"Duration" and "Calories" got a `0.922721`

correlation,
which is a very good correlation, and we can predict that the longer you work
out, the more calories you burn, and the other way around: if you burned a lot
of calories, you probably had a long work out.

### Bad Correlation:

"Duration" and "Maxpulse" got a `0.009403`

correlation,
which is a very bad correlation, meaning that we can not predict the max pulse
by just looking at the duration of the work out, and vice versa.

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