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Python Notes

Apr 01, 202310 min read

This is a rather old note. Now the better way to learn python is to find a problem you want to solve, and ask chatGPT about it.

Documentations:

Python built-in

Functions and callables

  • len(obj) return the number of items
  • type(obj) return the type of an object
  • dir(obj) return list of name in the current scope
  • sum(iterable, /, start=0) return summation of iterables
  • max/min(iterable, *[, key, default]) return maximum/minimum number of iterables
  • pow(base, exp[, mod]) return base to the power exp, mod makes a simpler way to say pow(base, exp) % mod
  • range(stop) or range(start, stop[, step]) range is an immutable sequence type
  • sorted(iterable, /, *, key=None, reverse=False) returns sorted iterables according to the key (function)
  • // floor div; % mod; ** exp; abs(num) absolute value

Containers

  • list [1, 2, 3, ‘a’]

    Indexed by integers,items are stored as stack (first in, last out)

    Operations: s[i:j:k]; s +t concatenation; s * num adding s to itself num times; s.index(x, i, j) index of x first occurrence of x in s in range i to j; x in s check if x is in s; s.count(x); del s[i:j] same as s[i:j] = []; s.clear(); s.copy() shallow copy; s.extend(); s.append(); s.insert(index, x), s.pop(index), s.remove(value); s.reverse() filter

  • tuple (1, 2, 3, ‘a’)

    Similar to list, but objects are not mutable

  • set {1, 2, 3, ‘a’}

    Objects in a set are unordered and unique, used for quick search. Set is implemented as a hash table, which supports lookup/insert/delete in O(1) time complexity.

  • dictionary { ‘key1’ : 1, ‘key2’: 2, ‘key3’: 3 }

    In older version of Python, dictionary is unordered, in python versions after 3.6, dictionary remembers the order of the key insertion Objects are key-value pairs, the keys are unique, immutable and can have different types. The values are mutable and can have different types.

    Operations:d[key]; key in d check if key exists;d.get(key[, default=None]) returns default if key doesn’t exist; d.keys(), d.values(), d.items() return iterables of keys, values and (key, val) paris; iter(d) ierate over keys; list(d) return list of all keys; del d[key]; d.pop(key[, default]); d.clear(); d.copy() shallow copy; d1 | d2 return merged dict;

Strings, lists, and tuples are sequence types that can use the +, *, +=, and *= operators.

Strings

  • count(substring[, start[, end]]) return counts of substring within start and end range
  • find(sub[, start[, end]]) return the lowest index of the substring within start and end range, return -1 if sub not found, rfind return the highest index.
  • join(iterable) return str concatenated with strings in iterable
  • strip([chars]) when omitting argument, remove the white space (including \n) at the beginning and end of a string, when specifying chars, remove all combination of the chars, lstrip() and rstrip() only remove white space from left or right.
  • split(sep=None, maxsplit=- 1) return a list of words in str, split by sep, when sep is None, runs of consecutive whitespace are regarded as a single separator
  • replace(old, new[, count]) return str after replacing old with new for count times.
  • maketrans() make a map for translate() to substitute or remove a set of characters, you can pass 0, 2 or 3 argument.
    • 1 argument: has to be dictionary, the keys (character or int ASCII number) will be replaced by value (character or int)
    • 2 arguments: both need to be string and need to have the same length, each char in string1 will be replace by the corresponding char in str2
    • 3 arguments: characters in the third string will be deleted, string 1&2 will do the same replacement
    • the map has to be passed into string.translate(transmap) to function
  • upper(), lower(), capitalize(), title() return all upper, all lower, first letter cap, and first letter cap for each word.

Class

class classname:
    def __init__(self, p1, p2, p3): #constructor
        self.property1 =  p1
        self.property2 =  p2
    def fuc1(self, arg1): #functions
    def fuc2(self, arg1, arg2, arg3, ...):

Miscellaneous

  • List, set and dictionary comprehension

    squarelist = [x ** 2 for x in range(5) if x%2==0]
squareset = {x**2 for x in range(5)}
squaredic = {x : x ** 2 for x in range(5)}

    comprehension can be nested [[expression for j in range(nj)] for i in range(ni)]

  • Map: list(map(float,[1,2,3])) same as [float(i) for i in [1,2,3]].

  • Lambda function: an anonymous inline function consisting of a single expression. lambda [parameters]: expression

  • *a_list unpack a list into separate positional arguments. e.g. func(*[1, 2]) same as func(1, 2)

  • File I/O open(file, mode='r', buffering=- 1, encoding=None, errors=None, newline=None, closefd=True, opener=None)

    • read data = [line.strip() for line in open('data.txt')] or use with open(file) as f:, which closes the file after.
    • write to file with open("data.txt",'a',newline='\n') as f: f.write(data)
    • pickle and JSON

Numpy & ndarray (N-dimensional array)

np.info() for help

Init

  • np.array(a_list, dtype=datatype)
  • np.arange([start, ]stop, [step, ]dtype=None, *, like=None)
  • np.linspace(start, stop, num=50, endpoint=True, dtype=None, axis=0)
  • np.random.random(d0, d1, ..., dn) Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).
  • np.zeros(shape) shape is int or tuple like (nrow, ncol)
  • np.ones(shape)
  • numpy.full(shape, fill_value, dtype=None, order='C', *, like=None)
  • np.eye(ndim) or np.identity(ndim) n-dimension identity matrix
  • np.empty(shape)

Attributes

  • arr.shape shape as tuple
  • arr.ndim n-dimension (like 1d, 2d or 3d arrays)
  • arr.size number of elements
  • arr.dtype and arr.dtype.name

Slicing and searching

  • arr[start:stop:step] applies to muliti dimenions matrix[start:stop:step, start:stop:step], : selects all
  • arr[::-1] is the same as np.flip(arr, axis=None), reverse the array
  • arr[[0, 1, 3]] return array with elements at index 0, 1 and 3.
  • arr[boolean list] return array with elements at True positions. arr[arr<1] selects all elements smaller than 1
  • numpy.where(condition, [x, y, ]/)
  • numpy.extract(condition, arr)[source]
  • np.ndindex(nrow, ncol) return zipped iterables row_index, col_index

Operations

  • arr.reshape(shape) return without changing arr
  • arr.resize(shape) change arr directly, no return
  • arr.ravel(), arr.flatten() and arr.reshape((-1,)), flatten returns a copy, reshape returns a view, ravel returns a view of the original array whenever possible.
  • np.concatenate((a1, a2, ...), axis=0), np.vstack((a1, a2)) or np.r_[a1, a2] stack row wise, np.hstack or np.c_[a1, a2] stack column wise, np.dstack are special cases, stacking arrays along axis = 0, 1, 2
  • np.vsplit(arr, obj), hsplit() Split the array vertically or horizontally. obj : index or slice
  • np.append(arr, values, axis=None) return a copy of appended array
  • np.insert(arr, obj, values, axis=None) obj : index, slice or sequence of ints
  • np.delete(arr, obj, axis=None) return new array
  • arr.copy() deep copy
  • arr.T or np.transpose(arr) return a copy of the transposed array.

Math functions

  • + - * / array arithmetic
  • np.mod(), exp(), log(), sqrt(), mean(), maximum(), minimum(), round(), floor(), ceil(), sin(), cos(), ..., full list
  • A @ B matrix multiplication
  • np.dot(), vdot(), cross() doc product and cross product
  • np.linalg.qr()， linalg.svd() matrix decomposition
  • np.linalg.eig() return eigenvalues and right eigenvectors

Random Generator

rng = np.random.default_rng()

  • rng.random(size=None) uniform random number from half-open interval [0.0, 1.0), customize range: rng.uniform(low=0.0, high=1.0, size=None)
  • rng.normal(mean=0.0, std=1.0, size=None) normal (Gaussian) distribution, rng.standard_normal(size) is a special case where mean=0 and std = 1
  • rng.binomial(n, p, size=None); random.Generator.poisson(lam=1.0, size=None)

Sorting and counting

  • arr.sort(axis=- 1, kind=None, order=None) sort array in place. np.sort(arr) return a copy
  • arr.argsort() or np.argsort(arr) return indices; argmin(), argmax(), np.argwhere()
  • numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None, *, equal_nan=True) return array of sorted unique elements, if index or counts are true, return zipped items array, index/counts

Numpy dtypes and converting

  • np.int_ == np.int64 (code ‘l’) and np.unit == np.uint64 (‘L’) unsigned; Other size np.(u)int8/16/32/64
  • np.float_ == np.double == np.float64(‘d’); Other size np.float16/32/64/128
  • np.complex_``np.cdouble == np.complex128 (‘D’) Complex number contain 2 64-bit-precision floating-point numbers.
  • np.bool_ == np.bool8 (code ’?‘)
  • np.str_ == np.unicode_ (‘U)
  • np.datetime64 (‘M’) and timedelta64 (‘m’)
  • np.object_ (‘O’)
  • arr.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
  • arr.tolist(); arr.tostring()

File I/O

  • Input numpy.genfromtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=" !#$%&'()*+, -./:;<=>?@[\\]^{|}~", replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None, encoding='bytes', *, ndmin=0, like=None)
  • Output numpy.savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', footer='', comments='# ', encoding=None)
  • arr.tofile(fid, sep='', format='%s') fid: filename or an open file object
  • np.savez(file, *args, **kwds) *args: arr1, arr2, …; Save several arrays into a single file in uncompressed .npz format. access : np.load(npzfile)
  • numpy.save(file, arr, allow_pickle=True, fix_imports=True) save one arr to a .npy file.

Pandas & DataFrame

Pandas DataFrame is essentially a spreadsheet / 2D array. Each column is a pandas Series.

Init

  • pd.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None) data can be ndarray, dict, iterable or DataFrame (copy=False for df).
  • pd.read_csv(file) options:
  • sep = ',' same as delimiter, header int, list of int, None; names column names; index_col; usecols specify cols (by list of name or index) to keep; skiprows; skipfooter; nrows num of rows to read; delim_whitespace=False and other options
  • pd.read_excel(file), pd.read_table(filename), pd.read_sql(query, connection_object), pd.read_json(json_string), pd.read_html(url)

Attributes

  • df.index return row index names as padas RangeIndex object, df.index.to_numpy() or to_list() convert to ndarray or list
  • df.columns return column name as Index object df.keys()
  • df.dtypes
  • df.values return ndarray of the spread sheet
  • df.shape return tuple of (nrow, ncol)
  • df.size return number of elements/cells
  • df.axes return a list [row index obj, columns]
  • df.ndim
  • df.empty return bool of whether df is empty

Indexing and slicing

  • df.at([row_label, col_label]) row labels are int by default, col labels are often strings; df.iat([row_index,col_index])
  • df.loc[]access a group of rows and columns by label(s) or a boolean array. Int numbers will be interpreted as a label and never the index position. Bool array needs to be the same length as the axis being sliced.
  • df.iloc[] similar to loc[], but arguments are indices instead of labels. df.iloc[0] slice first row, df.iloc[:, 0] first column, df.iloc[0,0] first cell. Note that df.iloc[0] return series and df.iloc[[0]] return dataframe.

Data Inspection

  • df.describe(percentiles=[.25,.5,.75]) for each column, display counts, min, max, std and percentiles
  • df.info() show index, dtype and memory
  • df.head(n) and df.tail(n) return the first and last n rows.
  • s.value_counts(dropna=Fase) unique values and counts for series df.value_count() counts unique rows
  • df.count(axis=0, level=None, numeric_only=False) count non-NA cells for each col or row, s.count() non-NA elements in series

Operations

  • df[new_col]=values add a new column
  • df.insert(loc, col_name, value, allow_duplicates=False) insert a column at index loc.
  • df.pop(col_name) return column and drop from frame. Raise KeyError if not found.
  • df.drop(labels, axis=0, index, columns, level, inplace=False) drop specified labels from rows or columns.axis=0 drop index, axis=1 drop columns
  • pd.concat(objs, axis=0, join='outer', ignore_index=False, sort=False, copy=True) concatenate pandas objects along a particular axis.
  • df.pivot(index=None, columns=None, values=None) return reshaped df organized by given index / column values.
  • pd.pivot_table(data, values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All', observed=False, sort=True) create a spreadsheet-style pivot table as df.
  • df.groupby(by, axis=0, as_index=True, sort=True, dropna=True) group df with columns or mapping function
  • df.apply(func, axis=0, raw=False) . e.g. df.apply(pd.Series.value_counts) return unique value and counts for all columns
  • df.copy() deep copy
  • df.dropna() df.fillna(val)

Iterations

  • for i in obj produce values if obj is Series, and column labels if obj is df
  • for index, Series in df.iterrows() Iterating through pandas objects is generally slow, and you should never modify the df, because depending on the data types, the iterator may return a copy and not a view.
  • df.items() act like dict.items() iterates through key-value (label-series) pairs.

Math functions

  • df.max(), min(), mean(), median(), std(), corr() std calculates variance, corr calculates correlation

File I/O

  • pandas has a set of reader and writer functions such as pd.read_csv() and df.to_csv(), the official guide is here

Graph View

Backlinks

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