Summarizing data-frame

To see the type, the information and summary of variables in the data-frame, use .dtypes, .describe(), and .info().

source ="https://storage.googleapis.com/mledu-datasets/california_housing_train.csv"
CHT = pd.read_csv(source, sep=",")
# show the type of variables
CHT.dtypes
# generate summary
CHT.describe()
CHT.info()

Beside the function print, pandas can show the first and the last part of data, using .head() and .tail(). By passing a number in the parenthesis, one can specify the output.

CHT.head(10)
CHT.tail(10)
CHT.sort_values(by='housing_median_age', ascending=False).head(3)
CHT.columns

It is easy to find the duplicates in data-frame and drop them, see below.

CHT.duplicated()
CHT.drop_duplicates()

To check the duplication in variables, specify their names as well,

CHT.duplicated(['longitude'])
CHT.drop_duplicates(['longitude'], keep='last')
CHT.index.duplicated()

Although .describe can give a summary of variables, more specific summery of variables (columns) can be extracted, see below.

CHT.count()
CHT[CHT.iloc[:,1]<34].nunique()

The following table includes the useful functions.

Function	Description
`count`	Number of non-null observations
`sum`	Sum of values
`mean`	Mean of value
`mad`	Mean absolute deviation
`median`	median of values
`min`	Minimum
`max`	Maximum
`mode`	Mode
`abs`	Absolute Value
`prod`	Product of values
`std`	Unbiased standard deviation
`var`	Unbiased variance
`sem`	Unbiased standard error of the mean
`skew`	Unbiased skewness (3rd moment)
`kurt`	Unbiased kurtosis (4th moment)
`quantile`	Sample quantile (value at %)
`cumsum`	Cumulative sum
`cumprod`	Cumulative product
`cummax`	Cumulative maximum
`cummin`	Cumulative minimum
`nunique`	number of unique elements
`value_counts`	Counts of unique values
`cov`	Calculate the covariance between columns
`corr`	Calculate the correlation between columns

The summaries can be obtained using any grouping variables in the data set:

CHT.groupby(['famlev']).groups.keys()
CHT.groupby(['famlev']).groups['H']
CHT.groupby(['famlev']).first()

CHT.groupby(['famlev']).sum()

CHT.groupby(['famlev'])['median_house_value'].sum()
# better output
CHT.groupby(['famlev'])[['median_house_value']].sum()

The grouped variables would be assigned as indices, to bring them back as variables use df.reset_index()

CHT.reset_index()

It is possible to apply even complex function, the following scripts calculate the coefficient of data.

def cv(x):
 return (np.mean(x)/np.var(x))

aggr = {
    'total_rooms':'sum',
    'population': lambda x: cv(x)
}
CHT.groupby('famlev').agg(aggr)

The output can be tidied up,

aggr = {
    'total_rooms':['mean','std']
}
grouped = CHT.groupby('famlev').agg(aggr)
grouped.columns = grouped.columns.droplevel(level=0)
grouped.rename(columns={"mean": "total_rooms", "std": "total_rooms"})
grouped.head()

The summarizations can be done using pivot table,

pd.pivot_table(CHT, index=['famlev'], aggfunc=['mean'])