```
In [2]:
```%pylab inline

```
```

```
In [3]:
```# Import libraries
from __future__ import absolute_import, division, print_function
# Ignore warnings
import warnings
#warnings.filterwarnings('ignore')
import sys
sys.path.append('tools/')
import numpy as np
import pandas as pd
import scipy.stats as st
# Graphing Libraries
import matplotlib.pyplot as pyplt
import seaborn as sns
sns.set_style("white")
# Configure for presentation
np.set_printoptions(threshold=50, linewidth=50)
import matplotlib as mpl
mpl.rc('font', size=16)
from IPython.display import display

```
In [4]:
```def axis_tick_frequency(ax, axis, freq):
"""The frequency of the y axis tick marks
Attributes
----------
ax: matplotlib axis object
axis: char eithher 'y' or 'x'
freq: int, the integer value of which the range moves
"""
if axis == 'y':
start, end = ax.get_ylim()
ax.yaxis.set_ticks(np.arange(start, end, freq))
elif axis == 'x':
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, freq))
else:
raise ValueError('{argument} is not a valid axis object'.format(argument=repr(axis)))

```
In [5]:
```def sample(num_sample, top, with_replacement=False):
"""
Create a random sample from a table
Attributes
---------
num_sample: int
top: dataframe
with_replacement: boolean
Returns a random subset of table index
"""
df_index = []
lst = np.arange(0, len(top), 1)
for i in np.arange(0, num_sample, 1):
# pick randomly from the whole table
sample_index = np.random.choice(lst)
if with_replacement:
# store index
df_index.append(sample_index)
else:
# remove the choice that was selected
lst = np.setdiff1d(lst,[sample_index])
df_index.append(sample_index)
return df_index

```
In [6]:
```baby_df = pd.read_csv('data/baby.csv')

```
In [7]:
```baby_df.head()

```
Out[7]:
```

```
In [8]:
```weight_smoke = baby_df[['Birth Weight', 'Maternal Smoker']]

```
In [9]:
```weight_smoke['Maternal Smoker'].value_counts()

```
Out[9]:
```

```
In [10]:
```smoker = baby_df['Maternal Smoker'] == True
non_smoker = baby_df['Maternal Smoker'] == False
df_non_smoker = baby_df.ix[baby_df[non_smoker].index, :]
df_non_smoker.columns = [u'Non Smoker Birth Weight', u'Gestational Days', u'Maternal Age',
u'Maternal Height', u'Maternal Pregnancy Weight', u'Maternal Smoker']
df_smoker = baby_df.ix[baby_df[smoker].index, :]
df_smoker.columns = [u'Smoker Birth Weight', u'Gestational Days', u'Maternal Age',
u'Maternal Height', u'Maternal Pregnancy Weight', u'Maternal Smoker']

```
In [11]:
```df_non_smoker['Non Smoker Birth Weight'].plot.hist(bins=np.arange(40, 186, 5), normed=True, alpha = 0.8)
df_smoker['Smoker Birth Weight'].plot.hist(bins=np.arange(40, 186, 5), normed=True, alpha = 0.8)
pyplt.ylabel("percent per ounce")
pyplt.xlabel("Birth Weight (ounce)")
pyplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);

```
```

Both distributions are approximately bell shaped and centered near 120 ounces. The distributions are not identical, of course, which raises the question of whether the difference reflects just chance variation or a difference in the distributions in the population.

**Null hypothesis**: In the population, the distribution of birth weights of babies is the same for mothers who don't smoke as for mothers who do. The difference in the sample is due to chance.

**Alternative hypothesis**: The two distributions are different in the population.

**Test statistic**: T test

Alternatively, we could use:

**Test statistic**: Birth weight is a quantitative variable, so it is reasonable to use the absolute difference between the means as the test statistic.
The observed value of the test statistic is about 9.27 ounces.

```
In [74]:
```a = df_non_smoker['Non Smoker Birth Weight'].values
b = df_smoker['Smoker Birth Weight'].values

```
In [49]:
```# difference in the means
a.mean() - b.mean()

```
Out[49]:
```

```
In [56]:
```raw = {
'Maternal Smoker': [False, True],
'Birth Weight mean': [123.085, 113.819]
}
means_table = pd.DataFrame(raw)
means_table

```
Out[56]:
```

```
In [36]:
```statistic, pvalue = st.ttest_ind(a, b)
print ('T statistic: %.2f'%statistic,'\nP-value:%.2f'%pvalue)

```
```

**P-value** is very, very small. As a result, we can *reject* the null hypothesis and conclude that in the population, the distribution of birth weights for babies of mothers who smoke and those that don't smoke are different.

Our A/B test has concluded that the two distributions are different, but that's a little unsatisfactory.

- How different are they?
- Which one has the larger mean?

These are natural questions that the test can't answer. Instead of just asking a yes/no question about whether the two distributions are different, we might learn more by not making any hypotheses and simply estimating the difference between the means.

The observed difference (nonsmokers −− smokers) was about **9.27 ounces**.

**The positive sign**says that the non-smoking mothers had larger babies on average.

But samples could have come out differently due to randomness. To see how different, we have to generate more samples; to generate more samples, we'll use the bootstrap. The bootstrap makes no hypotheses about whether or not the two distributions are the same. It simply replicates the original random sample and computes new values of the statistic.

The function `bootstrap_ci_means`

returns a bootstrap confidence interval for the **difference between the means** of the two groups in the population. In our example, the confidence interval would estimate the difference between the average birth weights of babies of mothers who didn't smoke and mothers who did, in the entire population.

The function returns an approximate 95% confidence interval for the difference between the two means, using the bootstrap percentile method.

```
In [32]:
```import scikits.bootstrap as bootstrap
import scipy

```
In [31]:
```# compute 95% confidence intervals around the mean
CIs = bootstrap.ci(baby_df[['Birth Weight', 'Maternal Smoker']], scipy.mean)
print ("Bootstrapped 95% confidence interval around the mean\nLow:", CIs[0], "\nHigh:", CIs[1])

```
```

```
In [58]:
```# bootstrap 5000 samples instead of only 1174
CIs = bootstrap.ci(baby_df[['Birth Weight', 'Maternal Smoker']], scipy.mean, n_samples=5000)
print ("Bootstrapped 95% confidence interval with 5,000 samples\nLow:", CIs[0], "\nHigh:", CIs[1])

```
```

```
In [181]:
```def get_means(df, variable, classes):
"""
Gets the means of a variable grouped by its class
Attributes
-------------
df: a pandas dataframe
variable: column
classes: column (bool)
"""
class_a = df[classes] == True
class_b = df[classes] == False
df_class_b = df.ix[df[class_b].index, :]
df_class_a = df.ix[df[class_a].index, :]
a = df_class_b[variable].values
b = df_class_a[variable].values
# difference in the means
a.mean() - b.mean()
raw = {
classes: [False, True],
variable: [a.mean(), b.mean()]
}
means_table = pd.DataFrame(raw)
return means_table

```
In [186]:
```def bootstrap_ci_means(table, variable, classes, repetitions):
"""Bootstrap approximate 95% confidence interval
for the difference between the means of the two classes
in the population
Attributes
-------------
table: a pandas dataframe
variable: column
classes: column (bool)
repetitions: int
"""
t = table[[variable, classes]]
mean_diffs = []
for i in np.arange(repetitions):
bootstrap_sampl = table.ix[sample(len(table), table, with_replacement=True), :]
m_tbl = get_means(bootstrap_sampl, variable, classes)
new_stat = m_tbl.ix[0, variable] - m_tbl.ix[1, variable]
mean_diffs = np.append(mean_diffs, new_stat)
left = np.percentile(mean_diffs, 2.5)
right = np.percentile(mean_diffs, 97.5)
# Find the observed test statistic
means_table = get_means(t, variable, classes)
obs_stat = means_table.ix[0, variable] - means_table.ix[1, variable]
df = pd.DataFrame()
df['Difference Between Means'] = mean_diffs
df.plot.hist(bins=20, normed=True)
plot([left, right], [0, 0], color='yellow', lw=8);
print('Observed difference between means:', obs_stat)
print('Approximate 95% CI for the difference between means:')
print(left, 'to', right)

```
In [187]:
```bootstrap_ci_means(baby_df, 'Birth Weight', 'Maternal Smoker', 5000)

```
```

*non-smoking* mothers had babies that weighed between 5.8 to 12.8 ounces larger than their smoking counter parts. Furthermore, because 0 is not included in the confidence interval between the difference in the means, we can tell that this distributions are different.

```
In [191]:
```bootstrap_ci_means(baby_df, 'Maternal Age', 'Maternal Smoker', 5000)

```
```

```
In [ ]:
```