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In cases when we don’t know how many arguments will be passed to a function, like when we want to pass a list or a tuple of values, we use *args.
def func(*args): for i in args: print(i) func(3,1,4,7) # You can change the number of arguments inside the function func
3
1
4
7
Using numpy library's random function you can create a random 1 dimensional array of any given size.
import numpy as np array = np.random.rand(5) print("1D Array filled with random values :", array)
1D Array filled with random values : [ 0.40537358 0.32104299 0.02995032 0.73725424 0.10978446]
list1 = [1, 2, 3, 4, 1, 1, 1, 4, 5]
print(list1.index(10))
Explanation:This line will throw a value error in python since when index command tries to get index of value 10" it is not able to find value 10 in the list and hence throws a value error. ValueError in Python means that there is a problem with the content of the object you are trying to access or assign the value to.
list1 = [1, 2, 3, 4, 1, 1, 1, 4, 5] print(list1.index(10))
list1 = [1, 2, 3, 4, 1, 1, 1, 4, 5] print(list1.index(4))
3
You can use the function listdir from os library in python to achieve the same.
import os os.listdir()
['.ipynb_checkpoints',
'Capture.PNG',
'cars.csv',
'foo.pdf',
'Pandas numpy matplotlib seaborn.ipynb']
Both pivot_table and groupby are used to aggregate your dataframe. The difference is only based on the shape of the result.
df = pd.DataFrame({"a": [1,2,3,1,2,3], "b":[1,1,1,2,2,2], "c":np.random.rand(6)})
pvt_tbl = pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum)
pvt_tbl
df.groupby(['a','b'])['c'].sum()
s = pd.Series(np.nan, index=[49,48,47,46,45, 1, 2, 3, 4, 5]) s.iloc[:3] # slice the first three row i.e. indexes 0,1,2 s.loc[:3] # slice up to and including index label 3
applymap() is used to apply a function to a Dataframe elementwise
df = pd.DataFrame([[4, 9],] * 3, columns=['A', 'B']) df.apply(np.sum, axis=0) df.apply(np.sum, axis=1) f = lambda x: x + 2 df.applymap(f)
cars = pd.read_csv('cars.csv') cars.head() f = lambda x: "low hp" if x< 100 else "high hp" cars['hp'] = cars.Horsepower.apply(f) cars.head()
A Matplotlib plot can be divided into following parts
The whole figure. The figure keeps track of all the child Axes, a smattering of ‘special’ artists (titles, figure legends, etc), and the canvas.A figure can have any number of Axes, but to be useful should have at least one.
This is what you think of as ‘a plot’, it is the region of the image with the data space (marked as the inner blue box). A given figure can contain many Axes, but a given Axes object can only be in one Figure. The Axes contains two (or three in the case of 3D) Axis objects (be aware of the difference between Axes and Axis) which take care of the data limits (the data limits can also be controlled via set via the set_xlim() and set_ylim() Axes methods). Each Axes has a title (set via set_title()), an xlabel (set via set_xlabel()), and a ylabel set via set_ylabel()).
These are the numberlinelike objects (circled in green). They take care of setting the graph limits and generating the ticks (the marks on the axis) and ticklabels (strings labeling the ticks). The location of the ticks is determined by a Locator object and the ticklabel strings are formatted by a Formatter. The combination of the correct Locator and Formatter gives very fine control over the tick locations and labels.
Basically everything you can see on the figure is an artist (even the Figure, Axes, and Axis objects). This includes Text objects, Line2D objects, collection objects, Patch objects ... (you get the idea). When the figure is rendered, all of the artists are drawn to the canvas. Most Artists are tied to an Axes; such an Artist cannot be shared by multiple Axes, or moved from one to another.
Subplots are grid of plots within a single figure. Subplots can be plotted using subplots() function from matplotlib.pyplot module.
x = np.linspace(0, 2 * np.pi, 400) y = np.sin(x ** 2)
## one figure with one subplot
fig, ax = plt.subplots(ncols=1,nrows=1) ax.plot(x, y) plt.plot()
fig1, ax1 = plt.subplots() ax1.hist(cars.Horsepower) plt.show()
# remove() removes the first matching value, not a specific index: a = [0, 2, 3, 2] a.remove(2) a
[0, 3, 2]
[0, 3, 2] a = [3, 2, 2, 1] del a[3] a
[3, 2, 2]
[3, 2, 2] a = [4, 3, 5] a.pop(1) a
[4, 5]
[4, 5]
x = ["1", "2", "3","new","old"] x.extend([4, 5]) print (x print("Length of list is :",len(x))
['1', '2', '3', 'new', 'old', 4, 5]
Length of list is : 7
x = ["1", "2", "3","new","old"] x.append([4, 5]) print (x) print("Length of list is :",len(x))
['1', '2', '3', 'new', 'old', [4, 5]]
Length of list is : 6
f = plt.figure() plt.plot(range(10), range(10), "o") plt.show()
f.savefig("foo.pdf")

cars.head()
type(cars)
pandas.core.frame.DataFrame
cars.dtypes
Car object
MPG float64
Cylinders int64
Displacement float64
Horsepower int64
Weight int64
Acceleration float64
Model int64
Origin object
hp object
dtype:object
fig, axarr = plt.subplots(2, sharex=True, sharey=True) axarr[0].plot(x, y) axarr[0].set_title('Subplot 1') axarr[1].scatter(x, y) axarr[1].set_title('Subplot 2')
The term broadcasting refers to the ability of NumPy to treat arrays of different shapes during arithmetic operations. If the dimensions of two arrays are dissimilar, elementtoelement operations are not possible. However, operations on arrays of nonsimilar shapes is still possible in NumPy, because of the broadcasting capability. The smaller array is broadcast to the size of the larger array so that they have compatible shapes. NumPy’s broadcasting rule relaxes this constraint when the arrays’ shapes meet certain constraints.
a = np.array([1.0, 2.0, 3.0]) b = np.array([2.0]) a * b
array([2., 4., 6.])
When operating on two arrays, NumPy compares their shapes elementwise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when
a = np.array([1.0, 2.0, 3.0]) b = np.array([5.0, 2.0]) a * b
The above line of code will generate 1 figure with 3 subplots arranged in 3 rows and one column with shared x axis.
fig, axarr = plt.subplots(3, sharex=True, sharey=True) fig.suptitle('Sharing both axes') axarr[0].plot(x, y) axarr[1].scatter(x, y) axarr[2].scatter(x, 2 * y ** 2  1, color='r')
When we want a good representation of the distribution of values in data we use swarmplot() from seaborn library in python. But refrain from using swarmplot in case you have large number of observations since it does not scale well to large numbers of observations.
import matplotlib.pyplot as plt import seaborn as sns sns.set_style("whitegrid")
tips = sns.load_dataset("tips") ax = sns.swarmplot(x="day", y="total_bill", data=tips) ax = sns.boxplot(x="day", y="total_bill", data=tips, showcaps=False,boxprops={'facecolor':'None'}, showfliers=False,whiskerprops={'linewidth':0}) plt.show()
Pickling is a process of converting a python object into a byte stream in order to store it in a file/database or to transport data over the network. Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
import pickle with open('filename', 'wb') as f: var = {1 : 'a' , 2 : 'b'} pickle.dump(var, f) #That would store the pickled version of our var dict in the 'filename' file in your home directory. #Then, in another script, you could load from this file into a variable and the dictionary would be recreated: with open('filename','rb') as f:
var = pickle.load(f)
You could use pickle library and use the following: dump(model,"file")
A sparse matrix can be generated by using rand function in scipy.sparse module.
rand(m, n, density=0.01, format='coo', dtype=None, random_state=None)
from scipy.sparse import rand matrix = rand(3, 4, density=0.25, format="dense", random_state=42) matrix
X = np.linspace(0,5,100) Y1 = X + 2*np.random.random(X.shape) Y2 = X**2 + np.random.random(X.shape)
fig1, ax1 = plt.subplots()
ax1.scatter(X,Y1,color='k') ax1.plot(X,Y2,color='g') plt.show()
cars.head()
fig1, ax1 = plt.subplots() ax1.scatter(cars.Horsepower,cars.Displacement,c=cars.Acceleration) plt.show()
help(pd.Series.loc)
Violin plots allow to visualize the distribution of a numeric variable for one or several groups. It is really close from a boxplot, but allows a deeper understanding of the density.
df = pd.DataFrame( {"Purchase": 50 * ["Yes"] + 50 * ["No"], "A": np.random.randint(1, 7, 100), "B": np.random.randint(1, 7, 100)} ) import seaborn as sns sns.violinplot(data=df[["A", "B"]], inner="quartile", bw=.15)
list.sort() sorts the list and save the sorted list, while sorted(list) returns a sorted copy of the list, without changing the original list. sorted() returns a new sorted list, leaving the original list unaffected. list.sort() sorts the list inplace, mutating the list indices, and returns None (like all inplace operations). sorted() works on any iterable, not just lists. Strings, tuples, dictionaries (you'll get the keys), generators, etc., returning a list containing all elements, sorted.
When to use sort() and when to use sorted()?
Which is fast to use for lists  sort() or sorted()?
Can a list's original positions be retrieved after list.sort()?
a = [3, 2, 1] a2 = sorted(a) print (a, a2)
[3, 2, 1] [1, 2, 3]
a3 = a.sort() print (a, a3)
[1, 2, 3] None
This function combines the matplotlib hist function (with automatic calculation of a good default bin size) with the seaborn kdeplot() and rugplot() functions. It can also fit scipy.stats distributions and plot the estimated PDF over the data.
import seaborn as sns sns.set(); np.random.seed(0) x = np.random.randn(100) ax = sns.distplot(x)
np.vstack(([1,2,3],[4,5,6]))
array([[1, 2, 3],
[4, 5, 6]])
np.column_stack(([1,2,3],[4,5,6]))
array([[1, 4],
[2, 5],
[3, 6]])
np.hstack(([1,2,3],[4,5,6]))
array([1, 2, 3, 4, 5, 6])
np.hstack(([[1],[2],[3]],[[4],[5],[6]]))
array([[1, 4],
[2, 5],
[3, 6]])
Pandas DataFrame object has a method astype() to cast to a specified dtype dtype.
cars.MPG[0:5]
0 18.0
1 15.0
2 18.0
3 16.0
4 17.0
Name: MPG, dtype: float64
cars.MPG = cars.MPG.astype('int')
cars.dtypes
Car object
MPG int64
Cylinders int64
Displacement float64
Horsepower int64
Weight int64
Acceleration float64
Model int64
Origin object
hp object
dtype: object
cars
cars.sort_index(axis=1,ascending=True).head(5)
cars.head()
cars.rename(columns={'Horsepower':'horse_power'},inplace=True)
cars.head()
cars.loc[cars.Car.isin(['AMC Hornet','Volkswagen Rabbit'])]
x = cars.Car.value_counts() # 'Chevrolet Impala'
x[x.index == 'Chevrolet Impala']
Chevrolet Impala 4
Name: Car, dtype: int64
Dash is a Python framework for building analytical web applications by Plotly.To build web applications using Dash we don't need JavaScript.
Describe() function Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
cars.describe()
cars.loc[cars.Car.isin(['AMC Hornet','Volkswagen Rabbit'])]
Pandas library provides read_csv() function to read a csv file. To skip first three rows we need to pass a value to argument skiprows to read_csv() function. skiprows can be list or integer.Line numbers to skip (0indexed) or number of lines to skip (int) at the start of the file.
pd.read_csv('cars.csv',skiprows=3)
The lambda operator or lambda function is a way to create small anonymous functions, i.e. functions without a name.Basic syntax of a lambda function is
. lambda argument list : expression.
The arguments list consists of a comma separated list of arguments and the expression is an arithmetic expression using these arguments.
In add =lambda x, y: x + y; x and y are arguments to the function and x + y is the expression which gets executed and its values is returned as output. lambda x, y: x + y returns a function object which can be assigned to any variable, in this case function object is assigned to the add variable. Mostly lambda functions are passed as parameters to a function which expects a function objects as parameter like map, reduce, filter functions.
add = lambda x, y : x + y add(1,2)
3
Seaborn provides five preset themes: white grid, dark grid, white, dark, and ticks, each suited to different applications and also personal preferences. Darkgrid is the default one. The White grid theme is similar but better suited to plots with heavy data elements, to switch to white grid.
sns.set_style("whitegrid") data = np.random.normal(size=(20, 6)) + np.arange(6) / 2 sns.boxplot(data=data)
sns.set_style("darkgrid") data = np.random.normal(size=(20, 6)) + np.arange(6) / 2 sns.boxplot(data=data)
Map applies a function to all the items in an input list map(function object, list1, list2,...) map functions expects a function object and any number of iterables.It executes the function over lists and returns a map object. list(map(lambda x : x*x, [1, 2, 3, 10]))
[1, 4, 9, 100] list(map(lambda x,y : x*y, [1, 2, 3, 10],[2,2,2,2]))
[2, 4, 6, 20]
List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.A list comprehension always returns a result list. Basic syntax : new list = [expression(i) for i in old list if filter(i)]
[x**2 for x in range(10)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
np.mean() Computes the arithmetic mean along the specified axis. It Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs
a = np.array([[1, 2], [3, 4]])
np.mean(a)
2.5
np.mean(a, axis=0)
array([2., 3.])
np.mean(a, axis=1)
array([1.5, 3.5])
Use subplots_adjust.When using subplots_adjust, the values of left, right, bottom and top are to be provided as fractions of the figure width and height. In additions, all values are measured from the left and bottom edges of the figure. This is why right and top can't be lower than left and bottom.
plt.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9) fig = plt.figure() fig.subplots_adjust(hspace=0.4, wspace=0.4)
for i in range(1, 7): ax = fig.add_subplot(2, 3, i) ax.text(0.5, 0.5, str((2, 3, i)), fontsize=18, ha='center')
Flask is a web micro framework for Python based on "Werkzeug, Jinja 2 and good intentions" BSD licensed and build for a small application with simpler requirements Werkzeug and Jinja are two of its dependencies. Flask is part of the microframework. Which means it will have little to no dependencies on external libraries. It mistakes the framework light while there is little dependency to update and less security bugs.
This plot lets you easily view both a joint distribution and its marginals at once. Joint distribution plots combine information from scatter plots and histograms to give you detailed information for bivariate distributions.
x, y = np.random.RandomState(8).multivariate_normal([0, 0], [(1, 0), (0, 1)], 1000).T
df = pd.DataFrame({"x":x,"y":y}) p = sns.jointplot(data=df,x='x', y='y') # kde plots a kernel density estimate in the margins and converts the interior into a shaded countour plot p = sns.jointplot(data=df,x='x', y='y',kind='kde')
Split() is used to break a large string down into smaller chunks, or strings. If no separator is defined when you call upon the function, whitespace will be used by default. In simpler terms, the separator is a defined character that will be placed between each variable.
x = "blue,red,green" x.split(",")
['blue', 'red', 'green']
# Create data data = {'score': [1,1,1,2,2,2,3,3,3]} # Create dataframe df = pd.DataFrame(data) # View dataframe df
# Calculate the moving average. That is, take the first two values, average them, then drop the first and add the third, etc. print(df)
score
0 1
1 1
2 1
3 2
4 2
5 2
6 3
7 3
8 3
print("ROLLING MEAN VALUES ARE:",df.rolling(window=2).mean())
ROLLING MEAN VALUES ARE: score
0 NaN
1 1.0
2 1.0
3 1.5
4 2.0
5 2.0
6 2.5
7 3.0
8 3.0
df = sns.load_dataset('iris') sns.regplot(x=df["sepal_length"], y=df["sepal_width"], fit_reg=False)
uniform_data = np.random.rand(10, 12) ax = sns.heatmap(uniform_data)
pd.read_excel('tmp.xlsx')
range() – This returns a list of numbers created using range() function.
xrange() – This function returns the generator object that can be used to display numbers only by looping. Only particular range is displayed on demand and hence called lazy evaluation. In Python 3, they removed the original range function and renamed xrange to a range.
To modify the strings, Python “re” module is providing 3 methods.
split()  uses a regex pattern to “split” a given string into a list.
sub()  finds all substrings where the regex pattern matches and then replace them with a different string
subn()  it is similar to sub() and also returns the new string along with the no. of replacements.
A[1, 4] access the data item at the 5th column, 2nd row.
readlines() : Reads all the lines and return each line as a string element in a list and readline() : Reads a line of the file and returns in form of a string. For specified n, reads at most n bytes. However, does not read more than one line, even if n exceeds the length of the line.
True. ndarray.dataitemSize is the buffer containing the actual elements of the array.
np.eye(3) whereas, library numpy has been imported as np.
df.to_csv(‘/file.csv’,encoding=’utf8′,index=False,header=False).
def MSE(real_target, predicted_target):
return np.mean((real_target – predicted_target)**2)
A tuple cannot be updated. Tuples are immutable. This means that you cannot change the values in a tuple once you have created it.
urllib2.urlopen(www.pythondeepdive.org) and requests.get(www.pythondeepdive.org) are the two forms to read the website.
set_index(‘String’)[‘Count’].to_dict()
You can put bookmark as time.sleep() so that you would know how much the code has slept, put bookmark as time.time() and check how much time elapses in each code line and copy whole code in an Ipython/Jupyter notebook, with each code line as a separate block and write function %%timeit in each block.
a.extend(b) function would do the job of converting it to one dimension.
An OrderedDict is a dictionary subclass that remembers the order in which its contents are added. If a new entry overwrites an existing entry, the original insertion position is left unchanged. Deleting an entry and reinserting it will move it to the end.
indall() function is used to return all the nonoverlapping matches of patterns in the string as the list of strings.
The partition method returns a 3tuple containing:
Here, the entire string has been passed as the separator hence the first and the last item of the tuple returned are null strings.
3 defines the precision of the floating point number.
2 is the view of original dataframe and 1 is a copy of original dataframe.
In cases when we don’t know how many arguments will be passed to a function, like when we want to pass a list or a tuple of values, we use *args.
def func(*args): for i in args: print(i) func(3,1,4,7) # You can change the number of arguments inside the function func
3
1
4
7
Using numpy library's random function you can create a random 1 dimensional array of any given size.
import numpy as np array = np.random.rand(5) print("1D Array filled with random values :", array)
1D Array filled with random values : [ 0.40537358 0.32104299 0.02995032 0.73725424 0.10978446]
list1 = [1, 2, 3, 4, 1, 1, 1, 4, 5]
print(list1.index(10))
Explanation:This line will throw a value error in python since when index command tries to get index of value 10" it is not able to find value 10 in the list and hence throws a value error. ValueError in Python means that there is a problem with the content of the object you are trying to access or assign the value to.
list1 = [1, 2, 3, 4, 1, 1, 1, 4, 5] print(list1.index(10))
list1 = [1, 2, 3, 4, 1, 1, 1, 4, 5] print(list1.index(4))
3
You can use the function listdir from os library in python to achieve the same.
import os os.listdir()
['.ipynb_checkpoints',
'Capture.PNG',
'cars.csv',
'foo.pdf',
'Pandas numpy matplotlib seaborn.ipynb']
Both pivot_table and groupby are used to aggregate your dataframe. The difference is only based on the shape of the result.
df = pd.DataFrame({"a": [1,2,3,1,2,3], "b":[1,1,1,2,2,2], "c":np.random.rand(6)})
pvt_tbl = pd.pivot_table(df, index=["a"], columns=["b"], values=["c"], aggfunc=np.sum)
pvt_tbl
df.groupby(['a','b'])['c'].sum()
s = pd.Series(np.nan, index=[49,48,47,46,45, 1, 2, 3, 4, 5]) s.iloc[:3] # slice the first three row i.e. indexes 0,1,2 s.loc[:3] # slice up to and including index label 3
applymap() is used to apply a function to a Dataframe elementwise
df = pd.DataFrame([[4, 9],] * 3, columns=['A', 'B']) df.apply(np.sum, axis=0) df.apply(np.sum, axis=1) f = lambda x: x + 2 df.applymap(f)
cars = pd.read_csv('cars.csv') cars.head() f = lambda x: "low hp" if x< 100 else "high hp" cars['hp'] = cars.Horsepower.apply(f) cars.head()
A Matplotlib plot can be divided into following parts
The whole figure. The figure keeps track of all the child Axes, a smattering of ‘special’ artists (titles, figure legends, etc), and the canvas.A figure can have any number of Axes, but to be useful should have at least one.
This is what you think of as ‘a plot’, it is the region of the image with the data space (marked as the inner blue box). A given figure can contain many Axes, but a given Axes object can only be in one Figure. The Axes contains two (or three in the case of 3D) Axis objects (be aware of the difference between Axes and Axis) which take care of the data limits (the data limits can also be controlled via set via the set_xlim() and set_ylim() Axes methods). Each Axes has a title (set via set_title()), an xlabel (set via set_xlabel()), and a ylabel set via set_ylabel()).
These are the numberlinelike objects (circled in green). They take care of setting the graph limits and generating the ticks (the marks on the axis) and ticklabels (strings labeling the ticks). The location of the ticks is determined by a Locator object and the ticklabel strings are formatted by a Formatter. The combination of the correct Locator and Formatter gives very fine control over the tick locations and labels.
Basically everything you can see on the figure is an artist (even the Figure, Axes, and Axis objects). This includes Text objects, Line2D objects, collection objects, Patch objects ... (you get the idea). When the figure is rendered, all of the artists are drawn to the canvas. Most Artists are tied to an Axes; such an Artist cannot be shared by multiple Axes, or moved from one to another.
Subplots are grid of plots within a single figure. Subplots can be plotted using subplots() function from matplotlib.pyplot module.
x = np.linspace(0, 2 * np.pi, 400) y = np.sin(x ** 2)
## one figure with one subplot
fig, ax = plt.subplots(ncols=1,nrows=1) ax.plot(x, y) plt.plot()
fig1, ax1 = plt.subplots() ax1.hist(cars.Horsepower) plt.show()
# remove() removes the first matching value, not a specific index: a = [0, 2, 3, 2] a.remove(2) a
[0, 3, 2]
[0, 3, 2] a = [3, 2, 2, 1] del a[3] a
[3, 2, 2]
[3, 2, 2] a = [4, 3, 5] a.pop(1) a
[4, 5]
[4, 5]
x = ["1", "2", "3","new","old"] x.extend([4, 5]) print (x print("Length of list is :",len(x))
['1', '2', '3', 'new', 'old', 4, 5]
Length of list is : 7
x = ["1", "2", "3","new","old"] x.append([4, 5]) print (x) print("Length of list is :",len(x))
['1', '2', '3', 'new', 'old', [4, 5]]
Length of list is : 6
f = plt.figure() plt.plot(range(10), range(10), "o") plt.show()
f.savefig("foo.pdf")

cars.head()
type(cars)
pandas.core.frame.DataFrame
cars.dtypes
Car object
MPG float64
Cylinders int64
Displacement float64
Horsepower int64
Weight int64
Acceleration float64
Model int64
Origin object
hp object
dtype:object
fig, axarr = plt.subplots(2, sharex=True, sharey=True) axarr[0].plot(x, y) axarr[0].set_title('Subplot 1') axarr[1].scatter(x, y) axarr[1].set_title('Subplot 2')
The term broadcasting refers to the ability of NumPy to treat arrays of different shapes during arithmetic operations. If the dimensions of two arrays are dissimilar, elementtoelement operations are not possible. However, operations on arrays of nonsimilar shapes is still possible in NumPy, because of the broadcasting capability. The smaller array is broadcast to the size of the larger array so that they have compatible shapes. NumPy’s broadcasting rule relaxes this constraint when the arrays’ shapes meet certain constraints.
a = np.array([1.0, 2.0, 3.0]) b = np.array([2.0]) a * b
array([2., 4., 6.])
When operating on two arrays, NumPy compares their shapes elementwise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when
a = np.array([1.0, 2.0, 3.0]) b = np.array([5.0, 2.0]) a * b
The above line of code will generate 1 figure with 3 subplots arranged in 3 rows and one column with shared x axis.
fig, axarr = plt.subplots(3, sharex=True, sharey=True) fig.suptitle('Sharing both axes') axarr[0].plot(x, y) axarr[1].scatter(x, y) axarr[2].scatter(x, 2 * y ** 2  1, color='r')
When we want a good representation of the distribution of values in data we use swarmplot() from seaborn library in python. But refrain from using swarmplot in case you have large number of observations since it does not scale well to large numbers of observations.
import matplotlib.pyplot as plt import seaborn as sns sns.set_style("whitegrid")
tips = sns.load_dataset("tips") ax = sns.swarmplot(x="day", y="total_bill", data=tips) ax = sns.boxplot(x="day", y="total_bill", data=tips, showcaps=False,boxprops={'facecolor':'None'}, showfliers=False,whiskerprops={'linewidth':0}) plt.show()
Pickling is a process of converting a python object into a byte stream in order to store it in a file/database or to transport data over the network. Pickle module accepts any Python object and converts it into a string representation and dumps it into a file by using dump function, this process is called pickling. While the process of retrieving original Python objects from the stored string representation is called unpickling.
import pickle with open('filename', 'wb') as f: var = {1 : 'a' , 2 : 'b'} pickle.dump(var, f) #That would store the pickled version of our var dict in the 'filename' file in your home directory. #Then, in another script, you could load from this file into a variable and the dictionary would be recreated: with open('filename','rb') as f:
var = pickle.load(f)
You could use pickle library and use the following: dump(model,"file")
A sparse matrix can be generated by using rand function in scipy.sparse module.
rand(m, n, density=0.01, format='coo', dtype=None, random_state=None)
from scipy.sparse import rand matrix = rand(3, 4, density=0.25, format="dense", random_state=42) matrix
X = np.linspace(0,5,100) Y1 = X + 2*np.random.random(X.shape) Y2 = X**2 + np.random.random(X.shape)
fig1, ax1 = plt.subplots()
ax1.scatter(X,Y1,color='k') ax1.plot(X,Y2,color='g') plt.show()
cars.head()
fig1, ax1 = plt.subplots() ax1.scatter(cars.Horsepower,cars.Displacement,c=cars.Acceleration) plt.show()
help(pd.Series.loc)
Violin plots allow to visualize the distribution of a numeric variable for one or several groups. It is really close from a boxplot, but allows a deeper understanding of the density.
df = pd.DataFrame( {"Purchase": 50 * ["Yes"] + 50 * ["No"], "A": np.random.randint(1, 7, 100), "B": np.random.randint(1, 7, 100)} ) import seaborn as sns sns.violinplot(data=df[["A", "B"]], inner="quartile", bw=.15)
list.sort() sorts the list and save the sorted list, while sorted(list) returns a sorted copy of the list, without changing the original list. sorted() returns a new sorted list, leaving the original list unaffected. list.sort() sorts the list inplace, mutating the list indices, and returns None (like all inplace operations). sorted() works on any iterable, not just lists. Strings, tuples, dictionaries (you'll get the keys), generators, etc., returning a list containing all elements, sorted.
When to use sort() and when to use sorted()?
Which is fast to use for lists  sort() or sorted()?
Can a list's original positions be retrieved after list.sort()?
a = [3, 2, 1] a2 = sorted(a) print (a, a2)
[3, 2, 1] [1, 2, 3]
a3 = a.sort() print (a, a3)
[1, 2, 3] None
This function combines the matplotlib hist function (with automatic calculation of a good default bin size) with the seaborn kdeplot() and rugplot() functions. It can also fit scipy.stats distributions and plot the estimated PDF over the data.
import seaborn as sns sns.set(); np.random.seed(0) x = np.random.randn(100) ax = sns.distplot(x)
np.vstack(([1,2,3],[4,5,6]))
array([[1, 2, 3],
[4, 5, 6]])
np.column_stack(([1,2,3],[4,5,6]))
array([[1, 4],
[2, 5],
[3, 6]])
np.hstack(([1,2,3],[4,5,6]))
array([1, 2, 3, 4, 5, 6])
np.hstack(([[1],[2],[3]],[[4],[5],[6]]))
array([[1, 4],
[2, 5],
[3, 6]])
Pandas DataFrame object has a method astype() to cast to a specified dtype dtype.
cars.MPG[0:5]
0 18.0
1 15.0
2 18.0
3 16.0
4 17.0
Name: MPG, dtype: float64
cars.MPG = cars.MPG.astype('int')
cars.dtypes
Car object
MPG int64
Cylinders int64
Displacement float64
Horsepower int64
Weight int64
Acceleration float64
Model int64
Origin object
hp object
dtype: object
cars
cars.sort_index(axis=1,ascending=True).head(5)
cars.head()
cars.rename(columns={'Horsepower':'horse_power'},inplace=True)
cars.head()
cars.loc[cars.Car.isin(['AMC Hornet','Volkswagen Rabbit'])]
x = cars.Car.value_counts() # 'Chevrolet Impala'
x[x.index == 'Chevrolet Impala']
Chevrolet Impala 4
Name: Car, dtype: int64
Dash is a Python framework for building analytical web applications by Plotly.To build web applications using Dash we don't need JavaScript.
Describe() function Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
cars.describe()
cars.loc[cars.Car.isin(['AMC Hornet','Volkswagen Rabbit'])]
Pandas library provides read_csv() function to read a csv file. To skip first three rows we need to pass a value to argument skiprows to read_csv() function. skiprows can be list or integer.Line numbers to skip (0indexed) or number of lines to skip (int) at the start of the file.
pd.read_csv('cars.csv',skiprows=3)
The lambda operator or lambda function is a way to create small anonymous functions, i.e. functions without a name.Basic syntax of a lambda function is
. lambda argument list : expression.
The arguments list consists of a comma separated list of arguments and the expression is an arithmetic expression using these arguments.
In add =lambda x, y: x + y; x and y are arguments to the function and x + y is the expression which gets executed and its values is returned as output. lambda x, y: x + y returns a function object which can be assigned to any variable, in this case function object is assigned to the add variable. Mostly lambda functions are passed as parameters to a function which expects a function objects as parameter like map, reduce, filter functions.
add = lambda x, y : x + y add(1,2)
3
Seaborn provides five preset themes: white grid, dark grid, white, dark, and ticks, each suited to different applications and also personal preferences. Darkgrid is the default one. The White grid theme is similar but better suited to plots with heavy data elements, to switch to white grid.
sns.set_style("whitegrid") data = np.random.normal(size=(20, 6)) + np.arange(6) / 2 sns.boxplot(data=data)
sns.set_style("darkgrid") data = np.random.normal(size=(20, 6)) + np.arange(6) / 2 sns.boxplot(data=data)
Map applies a function to all the items in an input list map(function object, list1, list2,...) map functions expects a function object and any number of iterables.It executes the function over lists and returns a map object. list(map(lambda x : x*x, [1, 2, 3, 10]))
[1, 4, 9, 100] list(map(lambda x,y : x*y, [1, 2, 3, 10],[2,2,2,2]))
[2, 4, 6, 20]
List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.A list comprehension always returns a result list. Basic syntax : new list = [expression(i) for i in old list if filter(i)]
[x**2 for x in range(10)]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
np.mean() Computes the arithmetic mean along the specified axis. It Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs
a = np.array([[1, 2], [3, 4]])
np.mean(a)
2.5
np.mean(a, axis=0)
array([2., 3.])
np.mean(a, axis=1)
array([1.5, 3.5])
Use subplots_adjust.When using subplots_adjust, the values of left, right, bottom and top are to be provided as fractions of the figure width and height. In additions, all values are measured from the left and bottom edges of the figure. This is why right and top can't be lower than left and bottom.
plt.subplots_adjust(left=0.1, right=0.9, bottom=0.1, top=0.9) fig = plt.figure() fig.subplots_adjust(hspace=0.4, wspace=0.4)
for i in range(1, 7): ax = fig.add_subplot(2, 3, i) ax.text(0.5, 0.5, str((2, 3, i)), fontsize=18, ha='center')
Flask is a web micro framework for Python based on "Werkzeug, Jinja 2 and good intentions" BSD licensed and build for a small application with simpler requirements Werkzeug and Jinja are two of its dependencies. Flask is part of the microframework. Which means it will have little to no dependencies on external libraries. It mistakes the framework light while there is little dependency to update and less security bugs.
This plot lets you easily view both a joint distribution and its marginals at once. Joint distribution plots combine information from scatter plots and histograms to give you detailed information for bivariate distributions.
x, y = np.random.RandomState(8).multivariate_normal([0, 0], [(1, 0), (0, 1)], 1000).T
df = pd.DataFrame({"x":x,"y":y}) p = sns.jointplot(data=df,x='x', y='y') # kde plots a kernel density estimate in the margins and converts the interior into a shaded countour plot p = sns.jointplot(data=df,x='x', y='y',kind='kde')
Split() is used to break a large string down into smaller chunks, or strings. If no separator is defined when you call upon the function, whitespace will be used by default. In simpler terms, the separator is a defined character that will be placed between each variable.
x = "blue,red,green" x.split(",")
['blue', 'red', 'green']
# Create data data = {'score': [1,1,1,2,2,2,3,3,3]} # Create dataframe df = pd.DataFrame(data) # View dataframe df
# Calculate the moving average. That is, take the first two values, average them, then drop the first and add the third, etc. print(df)
score
0 1
1 1
2 1
3 2
4 2
5 2
6 3
7 3
8 3
print("ROLLING MEAN VALUES ARE:",df.rolling(window=2).mean())
ROLLING MEAN VALUES ARE: score
0 NaN
1 1.0
2 1.0
3 1.5
4 2.0
5 2.0
6 2.5
7 3.0
8 3.0
df = sns.load_dataset('iris') sns.regplot(x=df["sepal_length"], y=df["sepal_width"], fit_reg=False)
uniform_data = np.random.rand(10, 12) ax = sns.heatmap(uniform_data)
pd.read_excel('tmp.xlsx')
range() – This returns a list of numbers created using range() function.
xrange() – This function returns the generator object that can be used to display numbers only by looping. Only particular range is displayed on demand and hence called lazy evaluation. In Python 3, they removed the original range function and renamed xrange to a range.
To modify the strings, Python “re” module is providing 3 methods.
split()  uses a regex pattern to “split” a given string into a list.
sub()  finds all substrings where the regex pattern matches and then replace them with a different string
subn()  it is similar to sub() and also returns the new string along with the no. of replacements.
A[1, 4] access the data item at the 5th column, 2nd row.
readlines() : Reads all the lines and return each line as a string element in a list and readline() : Reads a line of the file and returns in form of a string. For specified n, reads at most n bytes. However, does not read more than one line, even if n exceeds the length of the line.
True. ndarray.dataitemSize is the buffer containing the actual elements of the array.
np.eye(3) whereas, library numpy has been imported as np.
df.to_csv(‘/file.csv’,encoding=’utf8′,index=False,header=False).
def MSE(real_target, predicted_target):
return np.mean((real_target – predicted_target)**2)
A tuple cannot be updated. Tuples are immutable. This means that you cannot change the values in a tuple once you have created it.
urllib2.urlopen(www.pythondeepdive.org) and requests.get(www.pythondeepdive.org) are the two forms to read the website.
set_index(‘String’)[‘Count’].to_dict()
You can put bookmark as time.sleep() so that you would know how much the code has slept, put bookmark as time.time() and check how much time elapses in each code line and copy whole code in an Ipython/Jupyter notebook, with each code line as a separate block and write function %%timeit in each block.
a.extend(b) function would do the job of converting it to one dimension.
An OrderedDict is a dictionary subclass that remembers the order in which its contents are added. If a new entry overwrites an existing entry, the original insertion position is left unchanged. Deleting an entry and reinserting it will move it to the end.
indall() function is used to return all the nonoverlapping matches of patterns in the string as the list of strings.
The partition method returns a 3tuple containing:
Here, the entire string has been passed as the separator hence the first and the last item of the tuple returned are null strings.
3 defines the precision of the floating point number.
2 is the view of original dataframe and 1 is a copy of original dataframe.
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