summarice data

Question 1

Count number of rows with each unique value of variable

Answer 1

df[‘w’].value_counts()

Question 2

number of rows in DataFrame.

Answer 2

len(df)

Question 3

number of distinct values in a column.

Answer 3

df[‘w’].nunique()

Question 4

Basic descriptive statistics for each column (or GroupBy)

Answer 4

df.describe()

Question 5

Return the sum of the values for the requested axis

Answer 5

DataFrame.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)

Question 6

DataFrame.count(axis=0, level=None, numeric_only=False)

Answer 6

Count non-NA cells for each column or row.

Question 7

Return the median of the values for the requested axis

Answer 7

DataFrame.median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Question 8

Return values at the given quantile over requested axis, a la numpy.percentile.

Answer 8

DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation=’linear’)

Question 9

This method returns the minimum of the values in the object.

Answer 9

DataFrame.min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Question 10

This method returns the maximum of the values in the object.

Answer 10

DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Question 11

Return the mean of the values for the requested axis

Answer 11

DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Question 12

Return unbiased variance over requested axis.

Answer 12

DataFrame.var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Question 13

Return sample standard deviation over requested axis.

Answer 13

DataFrame.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)