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m sort an alphanumeric list `l` | sorted(l, key=lambda x: x.replace('0', 'Z')) |
plot logarithmic axes with matplotlib | ax.set_yscale('log') |
Access environment variable HOME | os.environ['HOME'] |
get value of environment variable HOME | os.environ['HOME'] |
print all environment variable | print(os.environ) |
get all environment variable | os.environ |
get value of the environment variable 'KEY_THAT_MIGHT_EXIST' | print(os.environ.get('KEY_THAT_MIGHT_EXIST')) |
get value of the environment variable 'KEY_THAT_MIGHT_EXIST' with default value `default_value` | print(os.getenv('KEY_THAT_MIGHT_EXIST', default_value)) |
get value of the environment variable 'HOME' with default value '/home/username/' | print(os.environ.get('HOME', '/home/username/')) |
eate a dictionary containing each string in list `my_list` split by '=' as a key/value pair | print(dict([s.split('=') for s in my_list])) |
find the index of element closest to number 11.5 in list `a` | min(enumerate(a), key=lambda x: abs(x[1] - 11.5)) |
find element `a` that contains string TEXT A in file `root` | e = root.xpath('.//a[contains(text(),"TEXT A")]') |
Find the`a` tag in html `root` which starts with the text `TEXT A` and assign it to `e` | e = root.xpath('.//a[starts-with(text(),"TEXT A")]') |
find the element that holds string 'TEXT A' in file `root` | e = root.xpath('.//a[text()="TEXT A"]') |
eate list `c` containing items from list `b` whose index is in list `index` | c = [b[i] for i in index] |
get the dot product of two one dimensional numpy array | np.dot(a[:, (None)], b[(None), :]) |
ltiplication of two 1dimensional arrays in numpy | np.outer(a, b) |
execute a file './abc.py' with arguments `arg1` and `arg2` in python shell | subprocess.call(['./abc.py', arg1, arg2]) |
Replace NaN values in column 'value' with the mean of data in column 'group' of dataframe `df` | df[['value']].fillna(df.groupby('group').transform('mean')) |
eparate each character in string `s` by '' | re.sub('(.)(?=.)', '\\1-', s) |
atenate '' in between characters of string `str` | re.sub('(?<=.)(?=.)', '-', str) |
get the indexes of the x and y axes in Numpy array `np` where variable `a` is equal to variable `value` | i, j = np.where(a == value) |
print letter that appears most frequently in string `s` | print(collections.Counter(s).most_common(1)[0]) |
find float number proceeding substring `par` in string `dir` | float(re.findall('(?:^|_)' + par + '(\\d+\\.\\d*)', dir)[0]) |
Get all the matches from a string `abcd` if it begins with a character `a` | re.findall('[^a]', 'abcd') |
get a list of variables from module 'adfix.py' in current module. | print([item for item in dir(adfix) if not item.startswith('__')]) |
get the first element of each tuple in a list `rows` | [x[0] for x in rows] |
get a list `res_list` of the first elements of each tuple in a list of tuples `rows` | res_list = [x[0] for x in rows] |
duplicate data in pandas dataframe `x` for 5 time | pd.concat([x] * 5, ignore_index=True) |
Get a repeated pandas data frame object `x` by `5` time | pd.concat([x] * 5) |
json `ips_data` by a key 'data_two' | sorted_list_of_keyvalues = sorted(list(ips_data.items()), key=item[1]['data_two']) |
ead json `elevations` to pandas dataframe `df` | pd.read_json(elevations) |
generate a random number in 1 to 7 with a given distribution [0.1, 0.05, 0.05, 0.2, 0.4, 0.2] | numpy.random.choice(numpy.arange(1, 7), p=[0.1, 0.05, 0.05, 0.2, 0.4, 0.2]) |
Return rows of data associated with the maximum value of column 'Value' in dataframe `df` | df.loc[df['Value'].idxmax()] |
find recurring patterns in a string '42344343434' | re.findall('^(.+?)((.+)\\3+)$', '42344343434')[0][:-1] |
vert binary string '\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@' to numpy array | np.fromstring('\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@', dtype='<f4') |
vert binary string to numpy array | np.fromstring('\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@', dtype='>f4') |
ert variables `(var1, var2, var3)` into sql statement 'INSERT INTO table VALUES (?, ?, ?)' | cursor.execute('INSERT INTO table VALUES (?, ?, ?)', (var1, var2, var3)) |
Execute a sql statement using variables `var1`, `var2` and `var3` | cursor.execute('INSERT INTO table VALUES (%s, %s, %s)', (var1, var2, var3)) |
w to use variables in SQL statement in Python? | cursor.execute('INSERT INTO table VALUES (%s, %s, %s)', (var1, var2, var3)) |
pandas split strings in column 'stats' by ',' into columns in dataframe `df` | df['stats'].str[1:-1].str.split(',', expand=True).astype(float) |
plit string in column 'stats' by ',' into separate columns in dataframe `df` | df['stats'].str[1:-1].str.split(',').apply(pd.Series).astype(float) |
Unpack column 'stats' in dataframe `df` into a series of colum | df['stats'].apply(pd.Series) |
wait for shell command `p` evoked by subprocess.Popen to complete | p.wait() |
encode string `s` to utf8 code | s.encode('utf8') |
parse string '01Jan1995' into a datetime object using format '%d%b%Y' | datetime.datetime.strptime('01-Jan-1995', '%d-%b-%Y') |
py a file from `src` to `dst` | copyfile(src, dst) |
py file /dir/file.ext to /new/dir/newname.ext | shutil.copy2('/dir/file.ext', '/new/dir/newname.ext') |
py file '/dir/file.ext' to '/new/dir' | shutil.copy2('/dir/file.ext', '/new/dir') |
print a list of integers `list_of_ints` using string formatting | print(', '.join(str(x) for x in list_of_ints)) |
ltiply column 'A' and column 'B' by column 'C' in datafram `df` | df[['A', 'B']].multiply(df['C'], axis='index') |
vert string 'a' to hex | hex(ord('a')) |
Get the sum of values to the power of their indices in a list `l` | sum(j ** i for i, j in enumerate(l, 1)) |
emove extra white spaces & tabs from a string `s` | """ """.join(s.split()) |
eplace comma in string `s` with empty string '' | s = s.replace(',', '') |
Resample dataframe `frame` to resolution of 1 hour `1H` for timeseries index, summing values in the column `radiation` averaging those in column `tamb` | frame.resample('1H').agg({'radiation': np.sum, 'tamb': np.mean}) |
w do I get rid of Python Tkinter root window? | root.destroy() |
eate a pandas dataframe `df` from elements of a dictionary `nvalues` | df = pd.DataFrame.from_dict({k: v for k, v in list(nvalues.items()) if k != 'y3'}) |
Flask get value of request variable 'firstname' | first_name = request.args.get('firstname') |
Flask get posted form data 'firstname' | first_name = request.form.get('firstname') |
get a list of substrings consisting of the first 5 characters of every string in list `buckets` | [s[:5] for s in buckets] |
list `the_list` by the length of string followed by alphabetical order | the_list.sort(key=lambda item: (-len(item), item)) |
Set index equal to field 'TRX_DATE' in dataframe `df` | df = df.set_index(['TRX_DATE']) |
List comprehension with an accumulator in range of 10 | list(accumulate(list(range(10)))) |
w to convert a date string '2013125' in format '%Y%m%d' to different format '%m/%d/%y' | datetime.datetime.strptime('2013-1-25', '%Y-%m-%d').strftime('%m/%d/%y') |
vert a date string '2013125' in format '%Y%m%d' to different format '%m/%d/%y' | datetime.datetime.strptime('2013-1-25', '%Y-%m-%d').strftime('%-m/%d/%y') |
get a dataframe `df2` that contains all the columns of dataframe `df` that do not end in `prefix` | df2 = df.ix[:, (~df.columns.str.endswith('prefix'))] |
eate list `new_list` containing the last 10 elements of list `my_list` | new_list = my_list[-10:] |
get the last 10 elements from a list `my_list` | my_list[-10:] |
vert matlab engine array `x` to a numpy ndarray | np.array(x._data).reshape(x.size[::-1]).T |
elect the first row grouped per level 0 of dataframe `df` | df.groupby(level=0, as_index=False).nth(0) |
atenate sequence of numpy arrays `LIST` into a one dimensional array along the first ax | numpy.concatenate(LIST, axis=0) |
vert and escape string \\xc3\\x85あ to UTF8 code | """\\xc3\\x85あ""".encode('utf-8').decode('unicode_escape') |
encode string \\xc3\\x85あ to byte | """\\xc3\\x85あ""".encode('utf-8') |
erleave the elements of two lists `a` and `b` | [j for i in zip(a, b) for j in i] |
erge two lists `a` and `b` into a single l | [j for i in zip(a, b) for j in i] |
delete all occureces of `8` in each string `s` in list `lst` | print([s.replace('8', '') for s in lst]) |
Split string `Hello` into a string of letters seperated by `,` | """,""".join('Hello') |
Django, select 100 random records from the database `Content.objects` | Content.objects.all().order_by('?')[:100] |
eate a NumPy array containing elements of array `A` as pointed to by index in array `B` | A[np.arange(A.shape[0])[:, (None)], B] |
pivot dataframe `df` so that values for `upc` become column headings and values for `saleid` become the index | df.pivot_table(index='saleid', columns='upc', aggfunc='size', fill_value=0) |
h zeroormore instances of lower case alphabet characters in a string `f233op ` | re.findall('([a-z]*)', 'f233op') |
h zeroormore instances of lower case alphabet characters in a string `f233op ` | re.findall('([a-z])*', 'f233op') |
plit string 'happy_hats_for_cats' using string '_for_' | re.split('_for_', 'happy_hats_for_cats') |
Split string 'sad_pandas_and_happy_cats_for_people' based on string 'and', 'or' or 'for' | re.split('_(?:for|or|and)_', 'sad_pandas_and_happy_cats_for_people') |
Split a string `l` by multiple words `for` or `or` or `and` | [re.split('_(?:f?or|and)_', s) for s in l] |
zip keys with individual values in lists `k` and `v` | [dict(zip(k, x)) for x in v] |
Sort a list 'lst' in descending order. | sorted(lst, reverse=True) |
array `order_array` based on column 'year', 'month' and 'day' | order_array.sort(order=['year', 'month', 'day']) |
Sort a structured numpy array 'df' on multiple columns 'year', 'month' and 'day'. | df.sort(['year', 'month', 'day']) |
heck if elements in list `my_list` are coherent in order | return my_list == list(range(my_list[0], my_list[-1] + 1)) |
group rows of pandas dataframe `df` with same 'id' | df.groupby('id').agg(lambda x: x.tolist()) |
encode `u'X\xc3\xbcY\xc3\x9f'` as unicode and decode with utf8 | 'X\xc3\xbcY\xc3\x9f'.encode('raw_unicode_escape').decode('utf-8') |
parse string `a` to flo | float(a) |
Parse String `s` to Float or I | try:
return int(s)
except ValueError:
return float(s) |
heck if object `a` has property 'property' | if hasattr(a, 'property'):
pass |
heck if object `a` has property 'property' | if hasattr(a, 'property'):
pass |
get the value of attribute 'property' of object `a` with default value 'default value' | getattr(a, 'property', 'default value') |
delete every 8th column in a numpy array 'a'. | np.delete(a, list(range(0, a.shape[1], 8)), axis=1) |
vert `ms` milliseconds to a datetime objec | datetime.datetime.fromtimestamp(ms / 1000.0) |