WIXLIB

Python For Data Science Cheat SheetNumPy BasicsLearn Python for Data Science Interactively at www.DataCamp.comNumPy2The NumPy library is the core library for scientific computing inPython. It provides a high-performance multidimensional arrayobject, and tools for working with these arrays. import numpy as npNumPy Arrays1232D array3D arrayaxis 1axis 01.523456axis 2axis 1axis 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 evenlyspaced values (step value) np.linspace(0,2,9)Create an array of evenlyspaced 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 arrayI/OSaving & 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 b.dtype.nameb.astype(int)Subsetting, Slicing, IndexingArray dimensionsLength of arrayNumber of array dimensionsNumber of array elementsData type of array elementsName of data typeConvert an array to a different typeSigned 64-bit integer typesStandard double-precision floating pointComplex numbers represented by 128 floatsBoolean type storing TRUE and FALSE valuesPython object typeFixed-length string typeFixed-length unicode typeSubsettingAsking For Help123Select the element at the 2nd index b[1,2]6.01.523456Select the element at row 1 column 2(equivalent to b[1][2]) a[0:2]123Select items at index 0 and 11.523Select items at rows 0 and 1 in column 14561.5234563Slicing b[0:2,1] np.info(np.ndarray.dtype)array([ 2.,Array MathematicsAlso 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 aarray([[1.5, 2., 3.]])array([[[ 3., 2., 1.],[ 4., 5., 6.]]]) g a - bSubtraction np.subtract(a,b) b aSubtractionAddition a[a 2] np.add(b,a) a / bAdditionDivision 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 rowsand columnsarray([[-0.5, 0. , 0. ],[-3. , -3. , -3. ]])array([[ 2.5,[ 5. ,4. ,7. ,array([[ 0.66666667, 1.[ 0.25, 0.4array([[[ 1.5,4. ,4. ,10. s(b)np.log(a)e.dot(f)array([[ 7.,[ 7.,array([3, 2, 1])6. ],9. ]]) np.divide(a,b) a * bCreating Arrays Arithmetic OperationsUse the following import convention:1D arrayInspecting Your Array, 1., 0.5],]])9. ],18. ionSquare rootPrint sines of an arrayElement-wise cosineElement-wise natural logarithmDot product7.],7.]]) a bElement-wise comparison a 2Element-wise comparison np.array equal(a, b)Array-wise comparisonarray([[False, True, True],[False, False, False]], dtype bool)array([True, False, False], dtype bool)Aggregate Functionsa.sum()a.min()b.max(axis 0)b.cumsum(axis e sumArray-wise minimum valueMaximum value of an array rowCumulative sum of the elementsMeanMedianCorrelation coefficientStandard deviationCopying Arrays h a.view() np.copy(a) h a.copy()23Fancy Indexingarray([ 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 dataCreate a copy of the arrayCreate a deep copy of the arraySort an arraySort the elements of an array's axisSelect elements from a less than 2Array ManipulationTransposing Array i np.transpose(b) i.TPermute array dimensionsPermute array dimensions b.ravel() g.reshape(3,-2)Flatten the arrayReshape, but don’t change data Return a new array with shape (2,6)Append items to an arrayInsert items in an arrayDelete items from an arrayChanging Array Shapeh.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 arraysarray([ 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 3rdindexSplit the array vertically at the 2nd indexarray([[ 1, 10],[ 2, 15],[ 3, 20]])Splitting ArraysSorting Arrays a.sort() c.sort(axis 0)1array([1])Adding/Removing ElementsComparison 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.]]])]DataCampLearn Python for Data Science Interactively

Data Wranglingwith pandasCheat Sheethttp://pandas.pydata.orgTidy Data – A foundation for wrangling in pandasFMAIn a tidydata set:&Each variable is savedin its own columnSyntax – Creating dy data complements pandas’s vectorizedoperations. pandas will automatically preserveobservations as you manipulate variables. Noother format works as intuitively with pandas.Each observation issaved in its own rowM*AM*AFdf.sort values('mpg')Order rows by values of a column (low to high).df.sort values('mpg',ascending False)Order rows by values of a column (high to low).pd.melt(df)Gather columns into rows.df.pivot(columns 'var', values 'val')Spread rows into columns.df.rename(columns {'y':'year'})Rename the columns of a DataFramedf.sort index()Sort the index of a DataFramedf.reset index()Reset index of DataFrame to row numbers, movingindex to columns.df pd.DataFrame([[4, 7, 10],[5, 8, 11],[6, 9, 12]],index [1, 2, 3],columns ['a', 'b', 'c'])Specify values for each row.aMReshaping Data – Change the layout of a data setdf pd.DataFrame({"a" : [4 ,5, 6],"b" : [7, 8, 9],"c" : [10, 11, 12]},index [1, 2, 3])Specify values for each column.nFpd.concat([df1,df2], axis 1)Append columns of DataFramespd.concat([df1,df2])Append rows of DataFramesSubset Observations (Rows)df.drop(columns ['Length','Height'])Drop columns from DataFrameSubset Variables (Columns)vdf pd.DataFrame({"a" : [4 ,5, 6],"b" : [7, 8, 9],"c" : [10, 11, 12]},index pd.MultiIndex.from tuples([('d',1),('d',2),('e',2)],names ['n','v'])))Create DataFrame with a MultiIndexMethod ChainingMost pandas methods return a DataFrame so thatanother pandas method can be applied to theresult. This improves readability of code.df (pd.melt(df).rename(columns {'variable' : 'var','value' : 'val'}).query('val 200'))df[df.Length 7]Extract rows that meet logicalcriteria.df.drop duplicates()Remove duplicate rows (onlyconsiders columns).df.head(n)Select first n rows.df.tail(n)Select last n rows.df.sample(frac 0.5)Randomly select fraction of rows.df.sample(n 10)Randomly select n rows.df.iloc[10:20]Select rows by position.df.nlargest(n, 'value')Select and order top n entries.df.nsmallest(n, 'value')Select and order bottom n entries.Logic in Python (and pandas) Less than! Not equal to Greater thandf.column.isin(values)Group membership Equalspd.isnull(obj)Is NaN Less than or equalspd.notnull(obj)Is not NaN Greater than or equals&, , , ,df.any(),df.all()Logical and, or, not, xor, any, alldf[['width','length','species']]Select multiple columns with specific names.df['width'] or df.widthSelect single column with specific name.df.filter(regex 'regex')Select columns whose name matches regular expression regex.regex (Regular Expressions) Examples'\.'Matches strings containing a period '.''Length 'Matches strings ending with word 'Length'' Sepal'Matches strings beginning with the word 'Sepal'' x[1-5] 'Matches strings beginning with 'x' and ending with 1,2,3,4,5'' (?!Species ).*'Matches strings except the string 'Species'df.loc[:,'x2':'x4']Select all columns between x2 and x4 (inclusive).df.iloc[:,[1,2,5]]Select columns in positions 1, 2 and 5 (first column is 0).df.loc[df['a'] 10, ['a','c']]Select rows meeting logical condition, and only the specific columns .http://pandas.pydata.org/ This cheat sheet inspired by Rstudio Data Wrangling Cheatsheet 2/data-wrangling-cheatsheet.pdf) Written by Irv Lustig, Princeton Consultants