Google Machine Learning Crash Course: Numerical data

california_housing_train__basic_statistics.txt

longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value
count	17000.0	17000.0	17000.0	17000.0	17000.0	17000.0	17000.0	17000.0	17000.0
mean	-119.6	35.6	28.6	2643.7	539.4	1429.6	501.2	3.9	207300.9
std	2.0	2.1	12.6	2179.9	421.5	1147.9	384.5	1.9	115983.8
min	-124.3	32.5	1.0	2.0	1.0	3.0	1.0	0.5	14999.0
25%	-121.8	33.9	18.0	1462.0	297.0	790.0	282.0	2.6	119400.0
50%	-118.5	34.2	29.0	2127.0	434.0	1167.0	409.0	3.5	180400.0
75%	-118.0	37.7	37.0	3151.2	648.2	1721.0	605.2	4.8	265000.0
max	-114.3	42.0	52.0	37937.0	6445.0	35682.0	6082.0	15.0	500001.0

numerical_data_bad_values.ipynb

numerical_data_stats.ipynb

# @title Setup - Import relevant modules

# The following code imports relevant modules that
# allow you to run the colab.
# If you encounter technical issues running some of the code sections
# that follow, try running this section again.

import pandas as pd

# The following lines adjust the granularity of reporting.
pd.options.display.max_rows = 10
pd.options.display.float_format = "{:.1f}".format


#@title Import the dataset

# The following code imports the dataset that is used in the colab.

training_df = pd.read_csv(filepath_or_buffer="https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv")


# Get statistics on the dataset.

# The following code returns basic statistics about the data in the dataframe.

training_df.describe()


# @title Solution (run this code block to view) { display-mode: "form" }

print("""The following columns might contain outliers:

  * total_rooms
  * total_bedrooms
  * population
  * households
  * possibly, median_income

In all of those columns:

  * the standard deviation is almost as high as the mean
  * the delta between 75% and max is much higher than the
      delta between min and 25%.""")