I was installing Talend and I realised that Talend installation requires Java 1.7 or below but not any Java version above that. (as at the time of writing) The installation quit straight away, giving a notification that it requires Java 1.7 These are the steps I followed to get Talend installation to succeed. First I…
Hi Guys, My mentor and Godfather in IT, Terry, showed me a simple and quick link to connect to Google Analytics API. Hope you also find it useful. Link is as below: https://ga-dev-tools.appspot.com/query-explorer/ To connect to your Google Analytics, Simply click on the link : https://ga-dev-tools.appspot.com/query-explorer/ Enter your login details for your Google Analytics account…
This is just to show that the import errors which were encountered during the installation of Crab, a Recommender Framework in Python worked fine with the fixes I earlier outlined. These were the errors and how they were fixed: ImportError: No module named ‘scikits ImportError: No module named learn.base The code below is the…
I was working on Recommender System and had a problem when i tried importing from scikits.crab.recommenders.knn import UserBasedRecommender. I encountered the following error ImportError: No module named learn.base I did research on the web and found one good solution but it was in Chinese. Here is the link and credit to the original fixer :…
If you have the above problem, to fix this, follow these guidelines as advised by Flowerowl at this link (https://github.com/muricoca/crab/issues/92): The steps are as follows: Don’t install crab in this way: pip install crab / easy_install crab You can find crab in pypi.python.org, this is not our scikits.crab source Solution: git clone https://github.com/muricoca/crab.git python setup.py…
Welcome Guys, We will be looking at Array transposition in this quick notes. This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy. In [1]:
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import numpy as np |
In [2]:
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arr = np.arange(50) arr |
Out[2]:
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array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]) |
In [17]:
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arr.reshape((10,5)) |
Out[17]:
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array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24], [25, 26, 27, 28, 29], [30, 31, 32, 33, 34], [35, 36, 37, 38, 39], [40, 41, 42, 43, 44], [45, 46, 47, 48, 49]]) |
In [10]:
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arr.T |
Out[10]:
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array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]) |
In [11]:
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np.dot(arr.T, arr) |
Out[11]:
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40425 |
In [23]:
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arr3d = np.arange(50).reshape((5,5,12)) |
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--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-23-8908d625ec66> in <module>() ----> 1 arr3d = np.arange(50).reshape((5,5,12)) ValueError: total size of new array must be unchanged |
…
Hi Guys, Thanks for all your emails. In this note, we will be looking at Array Processing in Python. This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy. In [3]:
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import numpy as np import matplotlib.pyplot as plt %matplotlib inline |
In [4]:
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points = np.arange(-5,5,0.01) |
In [5]:
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dx, dy = np.meshgrid(points,points) |
In [6]:
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dx |
Out[6]:
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array([[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], ..., [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99]]) |
In [7]:
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dy |
Out[7]:
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array([[-5. , -5. , -5. , ..., -5. , -5. , -5. ], [-4.99, -4.99, -4.99, ..., -4.99, -4.99, -4.99], [-4.98, -4.98, -4.98, ..., -4.98, -4.98, -4.98], ..., [ 4.97, 4.97, 4.97, ..., 4.97, 4.97, 4.97], [ 4.98, 4.98, 4.98, ..., 4.98, 4.98, 4.98], [ 4.99, 4.99, 4.99, ..., 4.99, 4.99, 4.99]]) |
In [8]:…
In this quick notes, we will go through aggregation in Python. This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy.
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import numpy as np import pandas as pd from pandas import Series, DataFrame |
url = “http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/” In [2]:
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dframe_wine = pd.read_csv('winequality-red.csv', sep=';') |
In [4]:
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dframe_wine.head() |
Out[4]: fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density…
In this quick notes, we will have a look at Rank and Sort in Series and DataFrames in Python In [1]:
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import numpy as np import pandas as pd from pandas import Series, DataFrame |
In [3]:
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#make a series ser1 = Series(range(3), index=['C','A','B']) ser1 |
Out[3]:
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C 0 A 1 B 2 dtype: int32 |
In [4]:
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#sort index of series ser1.sort_index() |
Out[4]:
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A 1 B 2 C 0 dtype: int32 |
In [5]:
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#order or sort by values ser1.order() |
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C:\Users\adaba\Anaconda3\lib\site-packages\ipykernel\__main__.py:2: FutureWarning: order is deprecated, use sort_values(...) from ipykernel import kernelapp as app |
Out[5]:
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C 0 A 1 B 2 dtype: int32 |
In [6]:
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ser1.sort_values() |
Out[6]:
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C 0 A 1 B 2 dtype: int32 |
In [7]:
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from numpy.random import randn |
In [8]:
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#make a series by passing in 10 random numbers ser2 = Series(randn(10)) ser2 |
Out[8]:
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0 -0.201751 1 1.534109 2 0.542825 3 1.421174 4 -0.173771 5 0.002573 6 -0.490208 7 -0.765340 8 -2.546040 9 -0.456699 dtype: float64 |
In [10]:
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#sort the values ser2.sort_values() |
…
This week we have a look at Data Alignment in Python In [1]:
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import numpy as np import pandas as pd from pandas import Series, DataFrame |
In [2]:
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#create series ser1 = Series([0,1,3], index=['A','B','C']) ser1 |
Out[2]:
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A 0 B 1 C 3 dtype: int64 |
In [3]:
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ser2 = Series([3,4,5,6], index=['A','B','C','D']) ser2 |
Out[3]:
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A 3 B 4 C 5 D 6 dtype: int64 |
In [4]:
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#adding two series which have different lenghts ser1 + ser2 |
Out[4]:
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A 3.0 B 5.0 C 8.0 D NaN dtype: float64 |
In [6]:
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#lets try and add dataframes #create dataframe dframe = DataFrame(np.arange(4).reshape((2,2)), columns=list('AB'), index=['LA','GA']) dframe |
Out[6]: A B LA 0 1 GA 2 3 In [10]:
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#create a second dataframe dframe2 = DataFrame(np.arange(9).reshape((3,3)), columns=list('ABC'), index=['LA','NYC','GA']) dframe2 |
Out[10]: A B C LA 0 1 2 NYC 3 4…
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