# How to calculate the p-value for a correlation coefficient in Pandas?

In order to calculate the p-value for a correlation coefficient in Pandas, you can use the .corr() method to compute the correlation coefficient, and then use the .pvalue() method to calculate the p-value. The p-value of a correlation coefficient indicates the probability of obtaining the observed correlation coefficient if the null hypothesis of no correlation is true. The lower the p-value, the stronger the evidence against the null hypothesis, indicating a stronger correlation between the two variables.

The can be used to measure the linear association between two variables.

This correlation coefficient always takes on a value between -1 and 1 where:

• -1: Perfectly negative linear correlation between two variables.
• 0: No linear correlation between two variables.
• 1: Perfectly positive linear correlation between two variables.

To determine if a correlation coefficient is statistically significant, you can calculate the corresponding t-score and p-value.

The formula to calculate the t-score of a correlation coefficient (r) is:

t = r√n-2 / √1-r2

The p-value is calculated as the corresponding two-sided p-value for the t-distribution with n-2 degrees of freedom.

To calculate the p-value for a Pearson correlation coefficient in pandas, you can use the pearsonr() function from the SciPy library:

```from scipy.stats import pearsonr

pearsonr(df['column1'], df['column2'])
```

This function will return the Pearson correlation coefficient between columns column1 and column2 along with the corresponding p-value that tells us whether or not the correlation coefficient is statistically significant.

If you would like to calculate the p-value for the Pearson correlation coefficient of each possible pairwise combination of columns in a DataFrame, you can use the following custom function to do so:

```def r_pvalues(df):
cols = pd.DataFrame(columns=df.columns)
p = cols.transpose().join(cols, how='outer')
for r in df.columns:
for c in df.columns:
tmp = df[df[r].notnull() & df[c].notnull()]
p[r][c] = round(pearsonr(tmp[r], tmp[c])[1], 4)
return p
```

The following examples show how to calculate p-values for correlation coefficients in practice with the following pandas DataFrame:

```import pandas as pd

#create DataFrame
df = pd.DataFrame({'x': [4, 5, 5, 7, 8, 10, 12, 13, 14, 15],
'y': [10, 12, 14, 18, np.nan, 19, 13, 20, 14, np.nan],
'z': [20, 24, 24, 23, 19, 15, 18, 14, 10, 12]})

#view DataFrame
print(df)

x     y   z
0   4  10.0  20
1   5  12.0  24
2   5  14.0  24
3   7  18.0  23
4   8   NaN  19
5  10  19.0  15
6  12  13.0  18
7  13  20.0  14
8  14  14.0  10
9  15   NaN  12
```

## Example 1: Calculate P-Value for Correlation Coefficient Between Two Columns in Pandas

The following code shows how to calculate the Pearson correlation coefficient and corresponding p-value for the x and y columns in the DataFrame:

```from scipy.stats import pearsonr

#drop all rows with NaN values
df_new = df.dropna()

#calculation correlation coefficient and p-value between x and y
pearsonr(df_new['x'], df_new['y'])

PearsonRResult(statistic=0.4791621985883838, pvalue=0.22961622926360523)
```

• The Pearson correlation coefficient is 0.4792.
• The corresponding p-value is 0.2296.

Since the correlation coefficient is positive, it indicates that there is a positive linear relationship between the two variables.

However, since the p-value of the correlation coefficient is not less than 0.05, the correlation is not statistically significant.

Note that we can also use the following syntax to extract the p-value for the correlation coefficient:

```#extract p-value of correlation coefficient
pearsonr(df_new['x'], df_new['y'])[1]

0.22961622926360523
```

The p-value for the correlation coefficient is 0.2296.

This matches the p-value from the previous output.

## Example 2: Calculate P-Value for Correlation Coefficient Between All Columns in Pandas

The following code shows how to calculate the Pearson correlation coefficient and corresponding p-value for each pairwise combination of columns in the pandas DataFrame:

```#create function to calculate p-values for each pairwise correlation coefficient
def r_pvalues(df):
cols = pd.DataFrame(columns=df.columns)
p = cols.transpose().join(cols, how='outer')
for r in df.columns:
for c in df.columns:
tmp = df[df[r].notnull() & df[c].notnull()]
p[r][c] = round(pearsonr(tmp[r], tmp[c])[1], 4)
return p

#use custom function to calculate p-values
r_pvalues(df)

x	     y	     z
x	   0.0	0.2296	0.0005
y	0.2296	   0.0	0.4238
z	0.0005	0.4238	   0.0```

From the output we can see:

• The p-value for the correlation coefficient between x and y is 0.2296.
• The p-value for the correlation coefficient between x and z is 0.0005.
• The p-value for the correlation coefficient between y and z is 0.4238.

Note that we rounded the p-values to four decimal places in our custom function.

Feel free to change the 4 in the last line of the function to a different number to round to a different number of decimal places.

Note: You can find the complete documentation for the SciPy pearsonr() function .

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