Python For Data Science Cheat Sheet Lists Also see NumPy ...

Python For Data Science Cheat Sheet

Python Basics

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Lists

>>>

>>>

>>>

>>>

Also see NumPy Arrays

a = 'is'

b = 'nice'

my_list = ['my', 'list', a, b]

my_list2 = [[4,5,6,7], [3,4,5,6]]

Selecting List Elements

Variables and Data Types

Subset

Variable Assignment

>>> x=5

>>> x

5

>>> my_list[1]

>>> my_list[-3]

Select item at index 1

Select 3rd last item

>>>

>>>

>>>

>>>

Select items at index 1 and 2

Select items after index 0

Select items before index 3

Copy my_list

Slice

Calculations With Variables

>>> x+2

Sum of two variables

>>> x-2

Subtraction of two variables

>>> x*2

Multiplication of two variables

>>> x**2

Exponentiation of a variable

>>> x%2

Remainder of a variable

>>> x/float(2)

Division of a variable

7

3

10

25

1

Index starts at 0

2.5

my_list[1:3]

my_list[1:]

my_list[:3]

my_list[:]

Subset Lists of Lists

>>> my_list2[1][0]

>>> my_list2[1][:2]

my_list[list][itemOfList]

str()

'5', '3.45', 'True'

Variables to strings

int()

5, 3, 1

Variables to integers

>>> my_list + my_list

['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']

>>> my_list * 2

['my', 'list', 'is', 'nice', 'my', 'list', 'is', 'nice']

>>> my_list2 > 4

float()

5.0, 1.0

Variables to floats

bool()

True, True, True

Variables to booleans

Asking For Help

>>> help(str)

my_list.index(a)

my_list.count(a)

my_list.append('!')

my_list.remove('!')

del(my_list[0:1])

my_list.reverse()

my_list.extend('!')

my_list.pop(-1)

my_list.insert(0,'!')

my_list.sort()

'thisStringIsAwesome'

String Operations

>>> my_string * 2

'thisStringIsAwesomethisStringIsAwesome'

>>> my_string + 'Innit'

'thisStringIsAwesomeInnit'

>>> 'm' in my_string

True

Machine learning

Scientific computing

2D plotting

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Numpy Arrays

Also see Lists

Selecting Numpy Array Elements

Subset

>>> my_array[1]

Get the index of an item

Count an item

Append an item at a time

Remove an item

Remove an item

Reverse the list

Append an item

Remove an item

Insert an item

Sort the list

Index starts at 0

Select item at index 1

Slice

>>> my_array[0:2]

Select items at index 0 and 1

array([1, 2])

Subset 2D Numpy arrays

>>> my_2darray[:,0]

my_2darray[rows, columns]

array([1, 4])

Numpy Array Operations

>>> my_array > 3

array([False, False, False,

>>> my_array * 2

True], dtype=bool)

array([2, 4, 6, 8])

>>> my_array + np.array([5, 6, 7, 8])

array([6, 8, 10, 12])

Strings

>>> my_string = 'thisStringIsAwesome'

>>> my_string

Data analysis

Install Python

2

List Methods

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

Import libraries

>>> import numpy

>>> import numpy as np

Selective import

>>> from math import pi

>>> my_list = [1, 2, 3, 4]

>>> my_array = np.array(my_list)

>>> my_2darray = np.array([[1,2,3],[4,5,6]])

List Operations

True

Types and Type Conversion

Libraries

String Operations

Index starts at 0

>>> my_string[3]

>>> my_string[4:9]

String Methods

>>>

>>>

>>>

>>>

>>>

String to uppercase

my_string.upper()

String to lowercase

my_string.lower()

Count String elements

my_string.count('w')

my_string.replace('e', 'i') Replace String elements

my_string.strip()

Strip whitespaces

Numpy Array Functions

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

my_array.shape

np.append(other_array)

np.insert(my_array, 1, 5)

np.delete(my_array,[1])

np.mean(my_array)

np.median(my_array)

my_array.corrcoef()

np.std(my_array)

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Get the dimensions of the array

Append items to an array

Insert items in an array

Delete items in an array

Mean of the array

Median of the array

Correlation coefficient

Standard deviation

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Jupyter Notebook

Working with Different Programming Languages

Widgets

Kernels provide computation and communication with front-end interfaces

like the notebooks. There are three main kernels:

Notebook widgets provide the ability to visualize and control changes

in your data, often as a control like a slider, textbox, etc.

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IRkernel

Installing Jupyter Notebook will automatically install the IPython kernel.

Saving/Loading Notebooks

Save current notebook

and record checkpoint

Preview of the printed

notebook

Close notebook & stop

running any scripts

Open an existing

notebook

Rename notebook

Revert notebook to a

previous checkpoint

You can use them to build interactive GUIs for your notebooks or to

synchronize stateful and stateless information between Python and

JavaScript.

Interrupt kernel

Restart kernel

Create new notebook

Make a copy of the

current notebook

IJulia

Interrupt kernel &

clear all output

Restart kernel & run

all cells

Connect back to a

remote notebook

Restart kernel & run

all cells

Download serialized

state of all widget

models in use

Save notebook

with interactive

widgets

Embed current

widgets

Run other installed

kernels

Command Mode:

Download notebook as

- IPython notebook

- Python

- HTML

- Markdown

- reST

- LaTeX

- PDF

15

13

1

2

3

4

5

6

7

8

9

10

11

14

12

Writing Code And Text

Code and text are encapsulated by 3 basic cell types: markdown cells, code

cells, and raw NBConvert cells.

Edit Cells

Cut currently selected cells

to clipboard

Paste cells from

clipboard above

current cell

Paste cells from

clipboard on top

of current cel

Revert ¡°Delete Cells¡±

invocation

Copy cells from

clipboard to current

cursor position

Paste cells from

clipboard below

current cell

Delete current cells

Split up a cell from

current cursor

position

Merge current cell

with the one above

Merge current cell

with the one below

Move current cell up

Move current cell

down

Adjust metadata

underlying the

current notebook

Remove cell

attachments

Paste attachments of

current cell

Find and replace

in selected cells

Copy attachments of

current cell

Insert image in

selected cells

Edit Mode:

Executing Cells

Run selected cell(s)

Run current cells down

and create a new one

above

Add new cell below the

current one

Run all cells

Run all cells above the

current cell

Run all cells below

the current cell

Change the cell type of

current cell

toggle, toggle

scrolling and clear

current outputs

toggle, toggle

scrolling and clear

all output

Toggle display of Jupyter

logo and filename

Toggle line numbers

in cells

Walk through a UI tour

List of built-in keyboard

shortcuts

Edit the built-in

keyboard shortcuts

Notebook help topics

Description of

markdown available

in notebook

Information on

unofficial Jupyter

Notebook extensions

IPython help topics

NumPy help topics

Toggle display of toolbar

Toggle display of cell

action icons:

- None

- Edit metadata

- Raw cell format

- Slideshow

- Attachments

- Tags

9. Interrupt kernel

10. Restart kernel

11. Display characteristics

12. Open command palette

13. Current kernel

14. Kernel status

15. Log out from notebook server

Asking For Help

Python help topics

View Cells

Insert Cells

Add new cell above the

current one

Run current cells down

and create a new one

below

1. Save and checkpoint

2. Insert cell below

3. Cut cell

4. Copy cell(s)

5. Paste cell(s) below

6. Move cell up

7. Move cell down

8. Run current cell

SciPy help topics

Matplotlib help topics

SymPy help topics

Pandas help topics

About Jupyter Notebook

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NumPy Basics

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NumPy

2

The NumPy library is the core library for scientific computing in

Python. It provides a high-performance multidimensional array

object, and tools for working with these arrays.

>>> import numpy as np

NumPy Arrays

1

2

3

2D array

3D array

axis 1

axis 0

1.5

2

3

4

5

6

axis 2

axis 1

axis 0

>>> a = np.array([1,2,3])

>>> b = np.array([(1.5,2,3), (4,5,6)], dtype = float)

>>> c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]],

dtype = float)

Initial Placeholders

>>> np.zeros((3,4))

Create an array of zeros

>>> np.ones((2,3,4),dtype=np.int16) Create an array of ones

>>> d = np.arange(10,25,5)

Create an array of evenly

spaced values (step value)

>>> np.linspace(0,2,9)

Create an array of evenly

spaced values (number of samples)

>>> e = np.full((2,2),7)

Create a constant array

>>> f = np.eye(2)

Create a 2X2 identity matrix

>>> np.random.random((2,2))

Create an array with random values

>>> np.empty((3,2))

Create an empty array

I/O

Saving & Loading On Disk

>>> np.save('my_array', a)

>>> np.savez('array.npz', a, b)

>>> np.load('my_array.npy')

Saving & Loading Text Files

>>> np.loadtxt("myfile.txt")

>>> np.genfromtxt("my_file.csv", delimiter=',')

>>> np.savetxt("myarray.txt", a, delimiter=" ")

Data Types

np.int64

np.float32

plex

np.bool

np.object

np.string_

np.unicode_

a.shape

len(a)

b.ndim

e.size

b.dtype

b.dtype.name

b.astype(int)

Subsetting, Slicing, Indexing

Array dimensions

Length of array

Number of array dimensions

Number of array elements

Data type of array elements

Name of data type

Convert an array to a different type

Signed 64-bit integer types

Standard double-precision floating point

Complex numbers represented by 128 floats

Boolean type storing TRUE and FALSE values

Python object type

Fixed-length string type

Fixed-length unicode type

Subsetting

Asking For Help

1

2

3

Select the element at the 2nd index

>>> b[1,2]

6.0

1.5

2

3

4

5

6

Select the element at row 0 column 2

(equivalent to b[1][2])

>>> a[0:2]

1

2

3

Select items at index 0 and 1

1.5

2

3

Select items at rows 0 and 1 in column 1

4

5

6

1.5

2

3

4

5

6

3

Slicing

>>> b[0:2,1]

>>> (np.ndarray.dtype)

array([ 2.,

Array Mathematics

Also see Lists

>>> a[2]

array([1, 2])

5.])

>>> b[:1]

>>> c[1,...]

Select all items at row 0

(equivalent to b[0:1, :])

Same as [1,:,:]

>>> a[ : :-1]

Reversed array a

array([[1.5, 2., 3.]])

array([[[ 3., 2., 1.],

[ 4., 5., 6.]]])

>>> g = a - b

Subtraction

>>> np.subtract(a,b)

>>> b + a

Subtraction

Addition

>>> a[a>> np.add(b,a)

>>> a / b

Addition

Division

>>> b[[1, 0, 1, 0],[0, 1, 2, 0]]

Select elements (1,0),(0,1),(1,2) and (0,0)

>>> b[[1, 0, 1, 0]][:,[0,1,2,0]]

Select a subset of the matrix¡¯s rows

and columns

array([[-0.5, 0. , 0. ],

[-3. , -3. , -3. ]])

array([[ 2.5,

[ 5. ,

4. ,

7. ,

array([[ 0.66666667, 1.

[ 0.25

, 0.4

array([[

[

>>>

>>>

>>>

>>>

>>>

>>>

>>>

1.5,

4. ,

4. ,

10. ,

np.multiply(a,b)

np.exp(b)

np.sqrt(b)

np.sin(a)

np.cos(b)

np.log(a)

e.dot(f)

array([[ 7.,

[ 7.,

array([3, 2, 1])

6. ],

9. ]])

>>> np.divide(a,b)

>>> a * b

Creating Arrays

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

Arithmetic Operations

Use the following import convention:

1D array

Inspecting Your Array

, 1.

, 0.5

],

]])

9. ],

18. ]])

Division

Multiplication

Multiplication

Exponentiation

Square root

Print sines of an array

Element-wise cosine

Element-wise natural logarithm

Dot product

7.],

7.]])

>>> a == b

Element-wise comparison

>>> a < 2

Element-wise comparison

>>> np.array_equal(a, b)

Array-wise comparison

array([[False, True, True],

[False, False, False]], dtype=bool)

array([True, False, False], dtype=bool)

Aggregate Functions

a.sum()

a.min()

b.max(axis=0)

b.cumsum(axis=1)

a.mean()

b.median()

a.corrcoef()

np.std(b)

Array-wise sum

Array-wise minimum value

Maximum value of an array row

Cumulative sum of the elements

Mean

Median

Correlation coefficient

Standard deviation

Copying Arrays

>>> h = a.view()

>>> np.copy(a)

>>> h = a.copy()

2

3

Fancy Indexing

array([ 4. , 2. , 6. , 1.5])

array([[ 4. ,5.

[ 1.5, 2.

[ 4. , 5.

[ 1.5, 2.

,

,

,

,

6.

3.

6.

3.

,

,

,

,

4. ],

1.5],

4. ],

1.5]])

Create a view of the array with the same data

Create a copy of the array

Create a deep copy of the array

Sort an array

Sort the elements of an array's axis

Select elements from a less than 2

Array Manipulation

Transposing Array

>>> i = np.transpose(b)

>>> i.T

Permute array dimensions

Permute array dimensions

>>> b.ravel()

>>> g.reshape(3,-2)

Flatten the array

Reshape, but don¡¯t change data

>>>

>>>

>>>

>>>

Return a new array with shape (2,6)

Append items to an array

Insert items in an array

Delete items from an array

Changing Array Shape

h.resize((2,6))

np.append(h,g)

np.insert(a, 1, 5)

np.delete(a,[1])

Combining Arrays

>>> np.concatenate((a,d),axis=0) Concatenate arrays

array([ 1,

2,

3, 10, 15, 20])

>>> np.vstack((a,b))

Stack arrays vertically (row-wise)

>>> np.r_[e,f]

>>> np.hstack((e,f))

Stack arrays vertically (row-wise)

Stack arrays horizontally (column-wise)

array([[ 1. ,

[ 1.5,

[ 4. ,

array([[ 7.,

[ 7.,

2. ,

2. ,

5. ,

7.,

7.,

3. ],

3. ],

6. ]])

1.,

0.,

0.],

1.]])

>>> np.column_stack((a,d))

Create stacked column-wise arrays

>>> np.c_[a,d]

Create stacked column-wise arrays

>>> np.hsplit(a,3)

Split the array horizontally at the 3rd

index

Split the array vertically at the 2nd index

array([[ 1, 10],

[ 2, 15],

[ 3, 20]])

Splitting Arrays

Sorting Arrays

>>> a.sort()

>>> c.sort(axis=0)

1

array([1])

Adding/Removing Elements

Comparison

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

Boolean Indexing

[array([1]),array([2]),array([3])]

>>> np.vsplit(c,2)

[array([[[ 1.5,

[ 4. ,

array([[[ 3.,

[ 4.,

2. , 1. ],

5. , 6. ]]]),

2., 3.],

5., 6.]]])]

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SciPy - Linear Algebra

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SciPy

The SciPy library is one of the core packages for

scientific computing that provides mathematical

algorithms and convenience functions built on the

NumPy extension of Python.

Interacting With NumPy

>>>

>>>

>>>

>>>

Also see NumPy

import numpy as np

a = np.array([1,2,3])

b = np.array([(1+5j,2j,3j), (4j,5j,6j)])

c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]])

Index Tricks

>>>

>>>

>>>

>>>

np.mgrid[0:5,0:5]

np.ogrid[0:2,0:2]

np.r_[[3,[0]*5,-1:1:10j]

np.c_[b,c]

Create a dense meshgrid

Create an open meshgrid

Stack arrays vertically (row-wise)

Create stacked column-wise arrays

Shape Manipulation

>>>

>>>

>>>

>>>

>>>

>>>

np.transpose(b)

b.flatten()

np.hstack((b,c))

np.vstack((a,b))

np.hsplit(c,2)

np.vpslit(d,2)

Permute array dimensions

Flatten the array

Stack arrays horizontally (column-wise)

Stack arrays vertically (row-wise)

Split the array horizontally at the 2nd index

Split the array vertically at the 2nd index

Polynomials

>>> from numpy import poly1d

>>> p = poly1d([3,4,5])

Create a polynomial object

Vectorizing Functions

>>> np.vectorize(myfunc)

Vectorize functions

Type Handling

>>>

>>>

>>>

>>>

np.real(c)

np.imag(c)

np.real_if_close(c,tol=1000)

np.cast['f'](np.pi)

Return the real part of the array elements

Return the imaginary part of the array elements

Return a real array if complex parts close to 0

Cast object to a data type

Other Useful Functions

(number of samples)

>>>

>>>

>>>

>>>

g [3:] += np.pi

np.unwrap(g)

Unwrap

np.logspace(0,10,3)

Create an array of evenly spaced values (log scale)

np.select([c>>

>>>

>>>

>>>

misc.factorial(a)

b(10,3,exact=True)

misc.central_diff_weights(3)

misc.derivative(myfunc,1.0)

>>> from scipy import linalg, sparse

Creating Matrices

>>>

>>>

>>>

>>>

A

B

C

D

=

=

=

=

Basic Matrix Routines

Inverse

>>>

>>>

>>>

>>>

>>>

A.I

linalg.inv(A)

A.T

A.H

np.trace(A)

Norm

>>> linalg.norm(A)

>>> linalg.norm(A,1)

>>> linalg.norm(A,np.inf)

Rank

conditions

Factorial

Combine N things taken at k time

Weights for Np-point central derivative

Find the n-th derivative of a function at a point

Inverse

Inverse

Tranpose matrix

Conjugate transposition

Trace

Frobenius norm

L1 norm (max column sum)

L inf norm (max row sum)

Matrix rank

>>> linalg.det(A)

Determinant

>>> linalg.solve(A,b)

>>> E = np.mat(a).T

>>> linalg.lstsq(D,E)

Solver for dense matrices

Solver for dense matrices

Least-squares solution to linear matrix

equation

Solving linear problems

Generalized inverse

>>> linalg.pinv(C)

Compute the pseudo-inverse of a matrix

(least-squares solver)

Compute the pseudo-inverse of a matrix

(SVD)

F = np.eye(3, k=1)

G = np.mat(np.identity(2))

C[C > 0.5] = 0

H = sparse.csr_matrix(C)

I = sparse.csc_matrix(D)

J = sparse.dok_matrix(A)

E.todense()

sparse.isspmatrix_csc(A)

Create a 2X2 identity matrix

Create a 2x2 identity matrix

Compressed Sparse Row matrix

Compressed Sparse Column matrix

Dictionary Of Keys matrix

Sparse matrix to full matrix

Identify sparse matrix

Sparse Matrix Routines

Inverse

Inverse

>>> sparse.linalg.norm(I)

Norm

>>> sparse.linalg.spsolve(H,I)

Solver for sparse matrices

Sparse Matrix Functions

>>> sparse.linalg.expm(I)

Asking For Help

>>> help(scipy.linalg.diagsvd)

>>> (np.matrix)

>>> np.subtract(A,D)

Subtraction

>>> np.divide(A,D)

Division

>>>

>>>

>>>

>>>

>>>

>>>

>>>

Multiplication

Dot product

Vector dot product

Inner product

Outer product

Tensor dot product

Kronecker product

Division

np.multiply(D,A)

np.dot(A,D)

np.vdot(A,D)

np.inner(A,D)

np.outer(A,D)

np.tensordot(A,D)

np.kron(A,D)

Exponential Functions

>>> linalg.expm(A)

>>> linalg.expm2(A)

>>> linalg.expm3(D)

Matrix exponential

Matrix exponential (Taylor Series)

Matrix exponential (eigenvalue

decomposition)

Logarithm Function

>>> linalg.logm(A)

Matrix logarithm

>>> linalg.sinm(D)

>>> linalg.cosm(D)

>>> linalg.tanm(A)

Matrix sine

Matrix cosine

Matrix tangent

>>> linalg.sinhm(D)

>>> linalg.coshm(D)

>>> linalg.tanhm(A)

Hypberbolic matrix sine

Hyperbolic matrix cosine

Hyperbolic matrix tangent

>>> np.sigm(A)

Matrix sign function

>>> linalg.sqrtm(A)

Matrix square root

>>> linalg.funm(A, lambda x: x*x)

Evaluate matrix function

Trigonometric Tunctions

Hyperbolic Trigonometric Functions

Matrix Sign Function

Matrix Square Root

Decompositions

Eigenvalues and Eigenvectors

>>> la, v = linalg.eig(A)

>>>

>>>

>>>

>>>

l1, l2 = la

v[:,0]

v[:,1]

linalg.eigvals(A)

Singular Value Decomposition

>>> sparse.linalg.inv(I)

Solving linear problems

Addition

Subtraction

Arbitrary Functions

Creating Sparse Matrices

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>>

>>> np.add(A,D)

Multiplication

>>> np.linalg.matrix_rank(C)

Determinant

Matrix Functions

Addition

np.matrix(np.random.random((2,2)))

np.asmatrix(b)

np.mat(np.random.random((10,5)))

np.mat([[3,4], [5,6]])

Norm

>>> np.angle(b,deg=True)

Return the angle of the complex argument

>>> g = np.linspace(0,np.pi,num=5) Create an array of evenly spaced values

Also see NumPy

You¡¯ll use the linalg and sparse modules. Note that scipy.linalg contains and expands on numpy.linalg.

>>> linalg.pinv2(C)

>>> def myfunc(a):

if a < 0:

return a*2

else:

return a/2

Linear Algebra

Sparse matrix exponential

Solve ordinary or generalized

eigenvalue problem for square matrix

Unpack eigenvalues

First eigenvector

Second eigenvector

Unpack eigenvalues

>>> U,s,Vh = linalg.svd(B)

Singular Value Decomposition (SVD)

>>> M,N = B.shape

>>> Sig = linalg.diagsvd(s,M,N) Construct sigma matrix in SVD

LU Decomposition

>>> P,L,U = linalg.lu(C)

LU Decomposition

Sparse Matrix Decompositions

>>> la, v = sparse.linalg.eigs(F,1)

>>> sparse.linalg.svds(H, 2)

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Eigenvalues and eigenvectors

SVD

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Pandas Basics

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Asking For Help

Selection

Also see NumPy Arrays

Getting

>>> s['b']

Get one element

>>> df[1:]

Get subset of a DataFrame

-5

Pandas

The Pandas library is built on NumPy and provides easy-to-use

data structures and data analysis tools for the Python

programming language.

Dropping

>>> help(pd.Series.loc)

1

2

Country

India

Brazil

Capital

New Delhi

Bras¨ªlia

Population

1303171035

207847528

By Position

>>> import pandas as pd

>>> df.iloc([0],[0])

'Belgium'

Pandas Data Structures

Index

a

3

b

-5

c

7

d

4

>>> s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd'])

DataFrame

Columns

Index

Select single value by row &

column

'Belgium'

A one-dimensional labeled array

capable of holding any data type

Country

0

Belgium

1

India

2

Brazil

Capital

Brussels

Population

11190846

New Delhi 1303171035

Bras¨ªlia

A two-dimensional labeled

data structure with columns

of potentially different types

207847528

>>> data = {'Country': ['Belgium', 'India', 'Brazil'],

'Capital': ['Brussels', 'New Delhi', 'Bras¨ªlia'],

'Population': [11190846, 1303171035, 207847528]}

>>> df = pd.DataFrame(data,

columns=['Country', 'Capital', 'Population'])

'Belgium'

Select single value by row &

column labels

>>> df.at([0], ['Country'])

'Belgium'

By Label/Position

>>> df.ix[2]

Select single row of

subset of rows

>>> df.ix[:,'Capital']

Select a single column of

subset of columns

>>> df.ix[1,'Capital']

Select rows and columns

Country

Brazil

Capital

Bras¨ªlia

Population 207847528

0

1

2

Brussels

New Delhi

Bras¨ªlia

Boolean Indexing

Setting

Set index a of Series s to 6

Read and Write to Excel

>>> pd.read_excel('file.xlsx')

>>> pd.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1')

Read multiple sheets from the same file

>>> xlsx = pd.ExcelFile('file.xls')

>>> df = pd.read_excel(xlsx, 'Sheet1')

(rows,columns)

Describe index

Describe DataFrame columns

Info on DataFrame

Number of non-NA values

>>>

>>>

>>>

>>>

>>>

>>>

>>>

df.sum()

df.cumsum()

df.min()/df.max()

df.idxmin()/df.idxmax()

df.describe()

df.mean()

df.median()

Sum of values

Cummulative sum of values

Minimum/maximum values

Minimum/Maximum index value

Summary statistics

Mean of values

Median of values

Applying Functions

>>> f = lambda x: x*2

>>> df.apply(f)

>>> df.applymap(f)

Apply function

Apply function element-wise

Internal Data Alignment

>>> s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd'])

>>> s + s3

a

10.0

c

5.0

b

d

NaN

7.0

Arithmetic Operations with Fill Methods

I/O

>>> pd.read_csv('file.csv', header=None, nrows=5)

>>> df.to_csv('myDataFrame.csv')

df.shape

df.index

df.columns

()

df.count()

NA values are introduced in the indices that don¡¯t overlap:

>>> s[~(s > 1)]

Series s where value is not >1

>>> s[(s < -1) | (s > 2)]

s where value is 2

>>> df[df['Population']>1200000000] Use filter to adjust DataFrame

Read and Write to CSV

>>>

>>>

>>>

>>>

>>>

Data Alignment

'New Delhi'

>>> s['a'] = 6

>>> df.sort_index()

Sort by labels along an axis

>>> df.sort_values(by='Country') Sort by the values along an axis

>>> df.rank()

Assign ranks to entries

Summary

By Label

>>> df.loc([0], ['Country'])

Sort & Rank

Basic Information

>>> df.iat([0],[0])

Series

Drop values from rows (axis=0)

>>> df.drop('Country', axis=1) Drop values from columns(axis=1)

Retrieving Series/DataFrame Information

Selecting, Boolean Indexing & Setting

Use the following import convention:

>>> s.drop(['a', 'c'])

Read and Write to SQL Query or Database Table

>>>

>>>

>>>

>>>

>>>

from sqlalchemy import create_engine

engine = create_engine('sqlite:///:memory:')

pd.read_sql("SELECT * FROM my_table;", engine)

pd.read_sql_table('my_table', engine)

pd.read_sql_query("SELECT * FROM my_table;", engine)

read_sql()is a convenience wrapper around read_sql_table() and

read_sql_query()

>>> pd.to_sql('myDf', engine)

You can also do the internal data alignment yourself with

the help of the fill methods:

>>> s.add(s3, fill_value=0)

a

b

c

d

10.0

-5.0

5.0

7.0

>>> s.sub(s3, fill_value=2)

>>> s.div(s3, fill_value=4)

>>> s.mul(s3, fill_value=3)

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