Pandas Sorting and statistics

"Python for Data analysis" sort sort_index ()

To sort rows or column indexes

In [1]: Import pandas as PD

in [2]: From pandas import Dataframe, Series in

[3]: obj = Series (range (4), index=[' d ' , ' A ', ' B ', ' C '] in

[4]: obj
out[4]:
d    0
a    1
b    2
c    3
Dtype:int64 In

[5]: Obj.sort_index ()
OUT[5]:
a    1
b    2
c    3
d    0
Dtype:int64 in

[6]: Import NumPy as NP In

[8]: frame = Dataframe (Np.arange (8). Reshape ((2,4)), index=[' three ', ' one '],
   ...:                   columns=[' d ', ' a ' , ' B ', ' C '] in

[9]: Frame
out[9]:
       d  a  b  c
three  0  1  2  3
one    4  5  6  7 in

[ten]: Frame.sort_index ()
out[10]:
       d  a  b  c
one    4  5  6  7
three  0 1 2 3 in  [one

]: Frame.sort_index (axis =1)
out[11]:
       a  b  c  D
three  1  2  3  0
one    5  6  7  4

in [[]: Frame.sort_index (Axis=1, Ascending=false)
out[12]:
       d  c  B  A
three  0  3  2  1
one    4  7  6 5

sort_values

The series are sorted by value , and any missing values are placed at the end of the series by default.

in [[]: obj = Series ([4, Np.nan, 6, Np.nan, -3, 2]) in

[[]: obj
out[19]:
0    4.0
1    nan
2    6.0
3    NaN
4   -3.0
5    2.0
dtype:float64 in

[O]: obj.sort_values ()
out [+]:
4   -3.0
5    2.0
0    4.0
2    6.0
1    nan
3    nan
Dtype:float64

On Dataframe, sorts by the values in one or more columns. You can achieve this by passing the name of one or more columns to the By option:

in [[]: Frame.sort_values (by= ' B ')
out[16]:
       d  a  b  c
three  0  1  2  3
One    4  5  6  7

Summary and Statistics

Sum, mean, max

options for reduction methods

Options	Description
Axis	Reduction of the axes. Dataframe row with 0, column with 1
Skipna	Exclude missing values, the default value is True
Level	If the axis is a hierarchical index (MILTIINDEX), group the reduction by level.

Indirect Statistics

Idxmin, Idxmax: An index that reaches the minimum or maximum value. Cumulative Type

Cumsum Summary statistics for a column

Df.describe: The numeric and non-numeric types are different. correlation coefficients and covariance

Corr (): Coefficient of correlation

CoV (): Covariance unique value, value count, and membership

Unique: You can get an array of unique values in series.

Isin: For determining the membership of a vector collection

Value_counts: Used to calculate the probability of each value appearing in a series.