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NumPy Cheat Sheet
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1 . Basi c Commands
Importing NumPy and checking
its version:
```python
import numpy as np
print(np.__version__)
```
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2. Array Creat i on
Creating NumPy arrays from lists and with
initial placeholders:
```python
# From a list
arr = np.array([1, 2, 3, 4, 5])
# Array of zeros
arr = np.zeros((3, 3))
# Array of ones
arr = np.ones((3, 3))
# Array with a range of values
arr = np.arange(0, 10)
# Array of random values
arr = np.random.rand(3, 3)
```
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3. Array At t ri but es
Getti ng an array' s shape and data type:
```python
arr = np. array([ [ 1, 2, 3] , [ 4, 5, 6] ] )
# Shape
pri nt(arr. shape)
# Data type
pri nt(arr. dtype)
```
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4. I ndexi ng and Sl i ci ng
Indexing and slicing one-dimensional and
multi-dimensional arrays:
```python
arr = np.array([1, 2, 3, 4, 5])
# Get the first element
print(arr[0])
# Get the last element
print(arr[-1])
# Get a slice from the second to the fourth
element
print(arr[1:4])
```
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5. Array Mani pul at i on
Vari ous ways to mani pul ate arrays such as
reshapi ng, stacki ng, and spl i tti ng:
```python
arr = np.array([ [ 1, 2, 3] , [ 4, 5, 6] ] )
# Reshape
arr_reshaped = arr.reshape((3, 2))
# Verti cal stack
arr_stack = np.vstack([ arr, arr] )
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6. Ari t hmet i c Operat i ons
Performing addition, subtraction, multiplication,
division, and dot product on arrays:
```python
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
# Addition print
(arr1 + arr2)
# Subtraction print
(arr1 - arr2)
# Multiplication print
(arr1 * arr2)
# Division print
(arr1 / arr2)
```
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7. St at i st i cal Operat i ons
Calculating the mean, median, and standard
deviation of an array:
```python
arr = np.array([1, 2, 3, 4, 5])
# Mean
print(np.mean(arr))
# Median
print(np.median(arr))
# Standard deviation
print(np.std(arr))
```
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Mat pl ot l i b Cheat Sheet
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1 . Basi c Commands
Matplot l i b i s a p l o t t i n g l i b rary for the
P y t hon p r o g r a m m i n g l a n g uage and its
n u m eri c a l m a t h e m a t i c s
e x t ens i o n N u m P y .
- I mport i n g Ma t p l o t l i b :
i mport ma t p l o t l i b . p y p l o t a s plt
- C hecki n g Ma t p l o t l i b v e r s ion:
i mport ma t p l o t l i b
p r i nt(ma t p l o tl i b . _ _ v e r s i o n __)
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2. Basi c Pl ot t i ng
Matplot l i b p r ov i d e s f u n c t i onalities for various types of plots.
- L ine P l o t : p lt . p l o t ( [ 1 , 2 , 3 , 4], [1, 4, 9, 16])
- S catte r P l o t: p l t . s c a t t e r ( [1, 2, 3, 4], [1, 4, 9, 16])
- B ar Pl o t : p l t. b a r ( [ ' g r o u p _ a', 'group_b', 'group_c'], [1, 10, 5])
- H istog r a m: p l t . h i s t ( [ 1 , 2 , 2, 3, 4, 4, 4, 5, 5, 5, 5])
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3. Fi gure and Axes
A f igure i n ma t p l o t l i b m e a ns the whole window in the user
i n t erfac e . A x is a r e t h e n u mber-line-like objects and they
t a k e car e o f ge n e r a t i n g t h e graph limits.
- C reati n g F i gu r e a n d A x e s : fig, ax = plt.subplots()
- S ettin g F i g ur e S i z e : f i g , ax = plt.subplots(figsize=( 6, 4))
- S ettin g A x i s L a b e l s a n d Title:
a x . set_x l a b e l( ' x ' )
a x . set_y l a b e l( ' y ' )
a x . set_t i t l e ( ' Ti t l e ' )
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4. Cust omi zi ng Pl ot s
Matplot l i b a l lo w s y o u t o c ustomize various aspects of
y o u r plo t s .
- C hangi n g L in e S t y l e a n d Color:
p l t .plot ( [ 1 , 2 , 3 , 4 ] , [ 1 , 4 , 9, 16], linestyle='--', color='r')
- A dding G r i d:
p l t .grid ( T r u e )
- S ettin g A x i s L i m i t s :
p l t .xlim( 0 , 5 )
p l t .ylim( 0 , 2 0 )
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5. Mul t i pl e Pl ot s
Matplot l i b p r ov i d e s f u n c t i onalities to create multiple
p l o ts in a s i n gl e f i g u r e .
- S ubplo t s :
f i g , axs = p l t .s u b p l o t s ( 2 )
- S harin g A x i s:
f i g , axs = p l t .s u b p l o t s ( 2 , s harex=True, sharey=True)
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6. Text and Annot at i ons
Matplot l i b p r ov i d e s f u n c t i onalities to add text and
a n n ota t i o n s t o t h e p l o t s .
- A dding T e x t:
p l t .text ( 0 . 5 , 0. 5 , ' H e l l o ' )
- A dding A n n ot a t i o n s :
p l t .anno t a t e ( 'H e l l o ' , x y = ( 0 . 5, 0.5), xytext=(0.6, 0.6),
a r r owp r o p s = d i c t ( f a c e c o l o r ='black', shrink=0.05))
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7. Savi ng Fi gures
Matplot l i b p r ov i d e s t h e s a vefig() function to save the
c u r rent f i g u r e t o a f i l e .
- S aving F i g ur e s a s P N G , P DF, SVG, and more:
p l t .save f i g ( ' f ig u r e . p n g ' )
p l t .save f i g ( ' f ig u r e . p d f ' )
p l t .save f i g ( ' f ig u r e . s v g ' )
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Pandas Cheat Sheet
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1 . Basi c Commands
Pandas is a software library for Python that provides
tools for data manipulation and analysis. It' s important
to ensure that the correct version of pandas is
installed for compatibility with your code.
- Importing pandas:
import pandas as pd
- Checking pandas version:
print(pd.__version__)
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2. Dataframe Creation
Dataframes are two-dimensional labeled data structures
with columns potentially of different types.
You can think of it like a spreadsheet or SQL table.
- From a list:
my_list = [1, 2, 3, 4, 5]
df = pd.DataFrame(my_list, columns=[' column_name' ])
- From a dictionary:
my_dict = {' A' : [1, 2, 3], ' B' : [4, 5, 6]}
df = pd.DataFrame(my_dict)
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3. Dat a Sel ect i on
Pandas provides different methods for data selection.
- Selecting a column:
df[' A' ]
- Selecting multiple columns:
df[[' A' , ' B' ]]
- Selecting rows:
df.loc[0] # row label
df.iloc[0] # row index
- Selecting specific value:
df.at[0, ' A' ] # row label and column name
df.iat[0, 0] # row index and column index
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4. Dat a Mani pul at i on
P a n das p r o v i d e v a r i o u s w a ys to manipulate a dataset.
- A dding a c ol u m n :
d f [ 'C'] = p d . S e r i e s ( [ 7 , 8 , 9 ])
- D eleti n g a co l u m n : d f . d r op('C', axis=1, inplace=True)
- R enami n g co l u m n s : d f . r ename(columns={'A': 'new_A '},
i n p lace= T r u e )
- A pplyi n g a fu n c t i o n t o a column:
d f [ 'A']. a p p l y ( la m b d a x : x * 2)
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5. Dat a Cl eani ng
D a t a cle a n i n g i s t h e p r o c e ss of detecting and correcting
( o r remo v i n g ) c o r r u p t o r i naccurate records from a
d a t ase t .
- C hecki n g f or n u l l v a l u e s :
d f . isnul l ( )
- D roppi n g n ul l v a l u e s :
d f . dropn a ( i n pl a c e = T r u e )
- F illin g n u l l va l u e s :
d f . filln a ( v a l u e= 0 , i n p l a c e =True)
- R eplac i n g va l u e s :
d f . repla c e ( 1 , 1 0 , i n p l a c e = True)
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6. Groupi ng & Aggregat i on
G r o upi n g i n v o l v e s c o m b i n i ng data based on some
c r i teria , w h i l e a g g r e g a t i o n is the process of turning the
r e s ults o f a qu e r y i n t o a s i ngle row.
- G roup b y :
d f . group b y ( ' A' )
- A ggreg a t i o n:
d f . agg({ ' A ' : [ ' mi n ' , ' m a x ' , ' m ean', 'sum']})
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7. Mergi ng, Joi ni ng, and
Concat enat i ng
Pandas provides various ways to combine DataFrames
including merge and join.
- Concatenating:
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})
df = pd.concat([df1, df2])
- Merging:
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [1, 2, 3], 'C': [7, 8, 9]})
df = pd.merge(df1, df2, on='A')
- Joining:
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'C': [7, 8, 9]})
df = df1.join(df2)
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8. Worki ng wi t h Dat es
Pandas provides powerful functionalities for working with
dates.
- Convert to datetime:
df['date'] = pd.to_datetime(df['date'])
- Extracting date parts:
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day
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9. Fi l e I /O
Pandas can seamlessly read from and write to a variety
of file formats.
- Reading a CSV file:
df = pd.read_csv('file.csv')
- Writing to a CSV file:
df.to_csv('file.csv', index=False)
- Similarly for other file formats like Excel (read_excel,
to_excel), JSON (read_json, to_json), SQL (read_sql,
to_sql), etc.
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