I have a pandas dataframe (call it data) with categorical and continuous values that look like this:
INDEX AGE SEX INCOME COUNTRY INSTANCE_WEIGHT
1 25 M 30000 USA 120
2 53 F 42000 FR 95
3 37 F 22000 USA 140
4 18 M 0 FR 110
.
.
.
15000 29 F 39000 USA 200
The instance weight indicates the number of people in the population that each record represents due to stratified sampling.
What I would like to do is plotting the distribution of each of the variable into an histogram. The problem is that I can't just plot an histogram of this current dataframe since it's not representative of the real distribution. To be representative, I have to multiply each row by its intance_weight before plotting it. The problem sounds easy but I can't find a good way of doing that.
A solution would be to duplicate each row instance_weight
times but the real dataframe is 300k rows and instance_weight
is around 1000.
This is the code I have for now to plot an histogram of each of the column.
fig = plt.figure(figsize=(20,70))
cols = 4
rows = ceil(float(data.shape[1]) / cols)
for i, column in enumerate(data.drop(["instance_weight","index"], axis=1).columns):
ax = fig.add_subplot(rows, cols, i + 1)
ax.set_title(column)
# Check if data categorical or not
if data.dtypes[column] == np.object:
data[column].value_counts().plot(kind="bar", axes=ax,
alpha=0.8, color=sns.color_palette(n_colors=1))
else:
data[column].hist(axes=ax, alpha=0.8)
plt.xticks(rotation="vertical")
plt.subplots_adjust(hspace=1, wspace=0.2)
How to consider the weight now?