Unlock 30% off on Manual Testing Annual Plans this Holiday Season.

Copied to Clipboard!

How to use extractall method in Playwright Python

Best Python code snippet using playwright-python

test_extract.py

Source:test_extract.py

...306    s = Series(["a3", "b3", "c2"], name="series_name", dtype=any_string_dtype)307    result = s.str.extract(r"(?P<letter>[a-z])", expand=True)308    expected = DataFrame({"letter": ["a", "b", "c"]}, dtype=any_string_dtype)309    tm.assert_frame_equal(result, expected)310def test_extractall(any_string_dtype):311    data = [312        "dave@google.com",313        "tdhock5@gmail.com",314        "maudelaperriere@gmail.com",315        "rob@gmail.com some text steve@gmail.com",316        "a@b.com some text c@d.com and e@f.com",317        np.nan,318        "",319    ]320    expected_tuples = [321        ("dave", "google", "com"),322        ("tdhock5", "gmail", "com"),323        ("maudelaperriere", "gmail", "com"),324        ("rob", "gmail", "com"),325        ("steve", "gmail", "com"),326        ("a", "b", "com"),327        ("c", "d", "com"),328        ("e", "f", "com"),329    ]330    pat = r"""331    (?P<user>[a-z0-9]+)332    @333    (?P<domain>[a-z]+)334    \.335    (?P<tld>[a-z]{2,4})336    """337    expected_columns = ["user", "domain", "tld"]338    s = Series(data, dtype=any_string_dtype)339    # extractall should return a DataFrame with one row for each match, indexed by the340    # subject from which the match came.341    expected_index = MultiIndex.from_tuples(342        [(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 0), (4, 1), (4, 2)],343        names=(None, "match"),344    )345    expected = DataFrame(346        expected_tuples, expected_index, expected_columns, dtype=any_string_dtype347    )348    result = s.str.extractall(pat, flags=re.VERBOSE)349    tm.assert_frame_equal(result, expected)350    # The index of the input Series should be used to construct the index of the output351    # DataFrame:352    mi = MultiIndex.from_tuples(353        [354            ("single", "Dave"),355            ("single", "Toby"),356            ("single", "Maude"),357            ("multiple", "robAndSteve"),358            ("multiple", "abcdef"),359            ("none", "missing"),360            ("none", "empty"),361        ]362    )363    s = Series(data, index=mi, dtype=any_string_dtype)364    expected_index = MultiIndex.from_tuples(365        [366            ("single", "Dave", 0),367            ("single", "Toby", 0),368            ("single", "Maude", 0),369            ("multiple", "robAndSteve", 0),370            ("multiple", "robAndSteve", 1),371            ("multiple", "abcdef", 0),372            ("multiple", "abcdef", 1),373            ("multiple", "abcdef", 2),374        ],375        names=(None, None, "match"),376    )377    expected = DataFrame(378        expected_tuples, expected_index, expected_columns, dtype=any_string_dtype379    )380    result = s.str.extractall(pat, flags=re.VERBOSE)381    tm.assert_frame_equal(result, expected)382    # MultiIndexed subject with names.383    s = Series(data, index=mi, dtype=any_string_dtype)384    s.index.names = ("matches", "description")385    expected_index.names = ("matches", "description", "match")386    expected = DataFrame(387        expected_tuples, expected_index, expected_columns, dtype=any_string_dtype388    )389    result = s.str.extractall(pat, flags=re.VERBOSE)390    tm.assert_frame_equal(result, expected)391@pytest.mark.parametrize(392    "pat,expected_names",393    [394        # optional groups.395        ("(?P<letter>[AB])?(?P<number>[123])", ["letter", "number"]),396        # only one of two groups has a name.397        ("([AB])?(?P<number>[123])", [0, "number"]),398    ],399)400def test_extractall_column_names(pat, expected_names, any_string_dtype):401    s = Series(["", "A1", "32"], dtype=any_string_dtype)402    result = s.str.extractall(pat)403    expected = DataFrame(404        [("A", "1"), (np.nan, "3"), (np.nan, "2")],405        index=MultiIndex.from_tuples([(1, 0), (2, 0), (2, 1)], names=(None, "match")),406        columns=expected_names,407        dtype=any_string_dtype,408    )409    tm.assert_frame_equal(result, expected)410def test_extractall_single_group(any_string_dtype):411    s = Series(["a3", "b3", "d4c2"], name="series_name", dtype=any_string_dtype)412    expected_index = MultiIndex.from_tuples(413        [(0, 0), (1, 0), (2, 0), (2, 1)], names=(None, "match")414    )415    # extractall(one named group) returns DataFrame with one named column.416    result = s.str.extractall(r"(?P<letter>[a-z])")417    expected = DataFrame(418        {"letter": ["a", "b", "d", "c"]}, index=expected_index, dtype=any_string_dtype419    )420    tm.assert_frame_equal(result, expected)421    # extractall(one un-named group) returns DataFrame with one un-named column.422    result = s.str.extractall(r"([a-z])")423    expected = DataFrame(424        ["a", "b", "d", "c"], index=expected_index, dtype=any_string_dtype425    )426    tm.assert_frame_equal(result, expected)427def test_extractall_single_group_with_quantifier(any_string_dtype):428    # GH#13382429    # extractall(one un-named group with quantifier) returns DataFrame with one un-named430    # column.431    s = Series(["ab3", "abc3", "d4cd2"], name="series_name", dtype=any_string_dtype)432    result = s.str.extractall(r"([a-z]+)")433    expected = DataFrame(434        ["ab", "abc", "d", "cd"],435        index=MultiIndex.from_tuples(436            [(0, 0), (1, 0), (2, 0), (2, 1)], names=(None, "match")437        ),438        dtype=any_string_dtype,439    )440    tm.assert_frame_equal(result, expected)441@pytest.mark.parametrize(442    "data, names",443    [444        ([], (None,)),445        ([], ("i1",)),446        ([], (None, "i2")),447        ([], ("i1", "i2")),448        (["a3", "b3", "d4c2"], (None,)),449        (["a3", "b3", "d4c2"], ("i1", "i2")),450        (["a3", "b3", "d4c2"], (None, "i2")),451        (["a3", "b3", "d4c2"], ("i1", "i2")),452    ],453)454def test_extractall_no_matches(data, names, any_string_dtype):455    # GH19075 extractall with no matches should return a valid MultiIndex456    n = len(data)457    if len(names) == 1:458        index = Index(range(n), name=names[0])459    else:460        tuples = (tuple([i] * (n - 1)) for i in range(n))461        index = MultiIndex.from_tuples(tuples, names=names)462    s = Series(data, name="series_name", index=index, dtype=any_string_dtype)463    expected_index = MultiIndex.from_tuples([], names=(names + ("match",)))464    # one un-named group.465    result = s.str.extractall("(z)")466    expected = DataFrame(columns=[0], index=expected_index, dtype=any_string_dtype)467    tm.assert_frame_equal(result, expected)468    # two un-named groups.469    result = s.str.extractall("(z)(z)")470    expected = DataFrame(columns=[0, 1], index=expected_index, dtype=any_string_dtype)471    tm.assert_frame_equal(result, expected)472    # one named group.473    result = s.str.extractall("(?P<first>z)")474    expected = DataFrame(475        columns=["first"], index=expected_index, dtype=any_string_dtype476    )477    tm.assert_frame_equal(result, expected)478    # two named groups.479    result = s.str.extractall("(?P<first>z)(?P<second>z)")480    expected = DataFrame(481        columns=["first", "second"], index=expected_index, dtype=any_string_dtype482    )483    tm.assert_frame_equal(result, expected)484    # one named, one un-named.485    result = s.str.extractall("(z)(?P<second>z)")486    expected = DataFrame(487        columns=[0, "second"], index=expected_index, dtype=any_string_dtype488    )489    tm.assert_frame_equal(result, expected)490def test_extractall_stringindex(any_string_dtype):491    s = Series(["a1a2", "b1", "c1"], name="xxx", dtype=any_string_dtype)492    result = s.str.extractall(r"[ab](?P<digit>\d)")493    expected = DataFrame(494        {"digit": ["1", "2", "1"]},495        index=MultiIndex.from_tuples([(0, 0), (0, 1), (1, 0)], names=[None, "match"]),496        dtype=any_string_dtype,497    )498    tm.assert_frame_equal(result, expected)499    # index should return the same result as the default index without name thus500    # index.name doesn't affect to the result501    if any_string_dtype == "object":502        for idx in [503            Index(["a1a2", "b1", "c1"]),504            Index(["a1a2", "b1", "c1"], name="xxx"),505        ]:506            result = idx.str.extractall(r"[ab](?P<digit>\d)")507            tm.assert_frame_equal(result, expected)508    s = Series(509        ["a1a2", "b1", "c1"],510        name="s_name",511        index=Index(["XX", "yy", "zz"], name="idx_name"),512        dtype=any_string_dtype,513    )514    result = s.str.extractall(r"[ab](?P<digit>\d)")515    expected = DataFrame(516        {"digit": ["1", "2", "1"]},517        index=MultiIndex.from_tuples(518            [("XX", 0), ("XX", 1), ("yy", 0)], names=["idx_name", "match"]519        ),520        dtype=any_string_dtype,521    )522    tm.assert_frame_equal(result, expected)523def test_extractall_no_capture_groups_raises(any_string_dtype):524    # Does not make sense to use extractall with a regex that has no capture groups.525    # (it returns DataFrame with one column for each capture group)526    s = Series(["a3", "b3", "d4c2"], name="series_name", dtype=any_string_dtype)527    with pytest.raises(ValueError, match="no capture groups"):528        s.str.extractall(r"[a-z]")529def test_extract_index_one_two_groups():530    s = Series(["a3", "b3", "d4c2"], index=["A3", "B3", "D4"], name="series_name")531    r = s.index.str.extract(r"([A-Z])", expand=True)532    e = DataFrame(["A", "B", "D"])533    tm.assert_frame_equal(r, e)534    # Prior to v0.18.0, index.str.extract(regex with one group)535    # returned Index. With more than one group, extract raised an536    # error (GH9980). Now extract always returns DataFrame.537    r = s.index.str.extract(r"(?P<letter>[A-Z])(?P<digit>[0-9])", expand=True)538    e_list = [("A", "3"), ("B", "3"), ("D", "4")]539    e = DataFrame(e_list, columns=["letter", "digit"])540    tm.assert_frame_equal(r, e)541def test_extractall_same_as_extract(any_string_dtype):542    s = Series(["a3", "b3", "c2"], name="series_name", dtype=any_string_dtype)543    pattern_two_noname = r"([a-z])([0-9])"544    extract_two_noname = s.str.extract(pattern_two_noname, expand=True)545    has_multi_index = s.str.extractall(pattern_two_noname)546    no_multi_index = has_multi_index.xs(0, level="match")547    tm.assert_frame_equal(extract_two_noname, no_multi_index)548    pattern_two_named = r"(?P<letter>[a-z])(?P<digit>[0-9])"549    extract_two_named = s.str.extract(pattern_two_named, expand=True)550    has_multi_index = s.str.extractall(pattern_two_named)551    no_multi_index = has_multi_index.xs(0, level="match")552    tm.assert_frame_equal(extract_two_named, no_multi_index)553    pattern_one_named = r"(?P<group_name>[a-z])"554    extract_one_named = s.str.extract(pattern_one_named, expand=True)555    has_multi_index = s.str.extractall(pattern_one_named)556    no_multi_index = has_multi_index.xs(0, level="match")557    tm.assert_frame_equal(extract_one_named, no_multi_index)558    pattern_one_noname = r"([a-z])"559    extract_one_noname = s.str.extract(pattern_one_noname, expand=True)560    has_multi_index = s.str.extractall(pattern_one_noname)561    no_multi_index = has_multi_index.xs(0, level="match")562    tm.assert_frame_equal(extract_one_noname, no_multi_index)563def test_extractall_same_as_extract_subject_index(any_string_dtype):564    # same as above tests, but s has an MultiIndex.565    mi = MultiIndex.from_tuples(566        [("A", "first"), ("B", "second"), ("C", "third")],567        names=("capital", "ordinal"),568    )569    s = Series(["a3", "b3", "c2"], index=mi, name="series_name", dtype=any_string_dtype)570    pattern_two_noname = r"([a-z])([0-9])"571    extract_two_noname = s.str.extract(pattern_two_noname, expand=True)572    has_match_index = s.str.extractall(pattern_two_noname)573    no_match_index = has_match_index.xs(0, level="match")574    tm.assert_frame_equal(extract_two_noname, no_match_index)575    pattern_two_named = r"(?P<letter>[a-z])(?P<digit>[0-9])"576    extract_two_named = s.str.extract(pattern_two_named, expand=True)577    has_match_index = s.str.extractall(pattern_two_named)578    no_match_index = has_match_index.xs(0, level="match")579    tm.assert_frame_equal(extract_two_named, no_match_index)580    pattern_one_named = r"(?P<group_name>[a-z])"581    extract_one_named = s.str.extract(pattern_one_named, expand=True)582    has_match_index = s.str.extractall(pattern_one_named)583    no_match_index = has_match_index.xs(0, level="match")584    tm.assert_frame_equal(extract_one_named, no_match_index)585    pattern_one_noname = r"([a-z])"586    extract_one_noname = s.str.extract(pattern_one_noname, expand=True)587    has_match_index = s.str.extractall(pattern_one_noname)588    no_match_index = has_match_index.xs(0, level="match")...

Week1_Assignment Text Analysis.py

Source:Week1_Assignment Text Analysis.py

...11        doc.append(line)12df = pd.Series(doc)13df.head(10)14# In[2]:15re1 = df.str.extractall(r'(?:(?P<Month>\d{1,2})[/-](?P<Day>\d{1,2})[/-](?P<Year>(?:19|20)?\d{2}))')16# In[3]:17re1['Month'] = re1['Month'].apply(lambda x: '0'+x if len(x) < 2 else x)18re1['Day'] = re1['Day'].apply(lambda x: '0'+x if len(x) < 2 else x)19re1['Year'] = re1['Year'].apply(lambda x: '19'+x if len(x) < 4 else x)20re1 = re1[re1.Day.astype(int) < 32]21re1 = re1[re1.Month.astype(int) < 13]22df1 = pd.DataFrame((re1['Month']+'/'+re1['Day']+'/'+re1['Year']).astype('datetime64'), columns = ['Date'])23df1.reset_index(inplace = True)24df1.drop(['match'], axis = 1, inplace = True)25df1 = df1.rename(columns={'level_0': 'Old_Index', 'Date': 'Date'})26df1.head()27# In[4]:28re2 = df.str.extractall(r'(?:(?P<Day>\d{2} )(?P<Month>(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z.,/ ]*) (?P<Year>(?:19|20)?\d{2}))') #set29# In[5]:30months = ({'January': '01', 'February': '02', 'March': '03', 'April': '04', 'May': '05', 'June': '06', 31              'July': '07', 'August': '08', 'September': '09', 'October': '10', 'November': '11', 'December': '12', 32              'Decemeber': '12', 'Janaury': '01',33              'Jan': '01', 'Feb': '02', 'Mar': '03', 'Apr': '04', 'May': '05', 'Jun': '06', 34              'Jul': '07', 'Aug': '08', 'Sep': '09', 'Oct': '10', 'Nov': '11', 'Dec': '12'})35re2['Month'] = re2['Month'].map(months)36re2['Day'] = re2['Day'].apply(lambda x: '0'+x if len(x) < 2 else x)37re2['Year'] = re2['Year'].apply(lambda x: '19'+x if len(x) < 4 else x)38re2 = re2[re2.Day.astype(int) < 32]39re2 = re2[re2.Month.astype(int) < 13]40df2 = pd.DataFrame((re2['Month']+'/'+re2['Day']+'/'+re2['Year']).astype('datetime64'), columns = ['Date'])41df2.reset_index(inplace = True)42df2.drop(['match'], axis = 1, inplace = True)43df2 = df2.rename(columns={'level_0': 'Old_Index', 'Date': 'Date'})44df2.head()45# In[6]:46re3 = df.str.extractall(r'(?:(?P<Month>(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z. ]*)(?P<Day>\d{2}[/., ] )(?P<Year>(?:19|20)?\d{2}))') #set47# In[7]:48re3['Month'] = re3['Month'].str.replace(".", "").str.strip()49re3['Day'] = re3['Day'].str.replace(",", "")50re3['Month'] = re3['Month'].map(months)51re3['Day'] = re3['Day'].apply(lambda x: '0'+x if len(x) < 2 else x)52re3['Year'] = re3['Year'].apply(lambda x: '19'+x if len(x) < 4 else x)53re3 = re3[re3.Day.astype(int) < 32]54re3 = re3[re3.Month.astype(int) < 13]55df3 = pd.DataFrame((re3['Month']+'/'+re3['Day']+'/'+re3['Year']).astype('datetime64'), columns = ['Date'])56df3.reset_index(inplace = True)57df3.drop(['match'], axis = 1, inplace = True)58df3 = df3.rename(columns={'level_0': 'Old_Index', 'Date': 'Date'})59df3.head()60# In[8]:61re4 = df.str.extractall(r'(?:(?P<Month>(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z. ]*)(?P<Day>\d{2} )(?P<Year>(?:19|20)?\d{2}))') #set62# In[9]:63re4['Month'] = re4['Month'].str.replace(".", "").str.strip()64re4['Day'] = re4['Day'].str.replace(",", "")65re4['Month'] = re4['Month'].map(months)66re4['Day'] = re4['Day'].apply(lambda x: '0'+x if len(x) < 2 else x)67re4['Year'] = re4['Year'].apply(lambda x: '19'+x if len(x) < 4 else x)68re4 = re4[re4.Day.astype(int) < 32]69re4 = re4[re4.Month.astype(int) < 13]70df4 = pd.DataFrame((re4['Month']+'/'+re4['Day']+'/'+re4['Year']).astype('datetime64'), columns = ['Date'])71df4.reset_index(inplace = True)72df4.drop(['match'], axis = 1, inplace = True)73df4 = df4.rename(columns={'level_0': 'Old_Index', 'Date': 'Date'})74df4.head()75# In[10]:76re5 = df.str.extractall(r'(?:(?P<Month>(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z./, ]*)(?P<Day>)(?P<Year>(?:19|20)\d{2}))') #set77# In[11]:78re5['Month'] = re5['Month'].str.replace(".", "").str.strip()79re5['Month'] = re5['Month'].str.replace(",", "").str.strip()80re5['Month'] = re5['Month'].map(months)81re5['Day'] = re5['Day'].replace(np.nan, '01', regex = True)82re5['Year'] = re5['Year'].apply(lambda x: '19'+x if len(x) < 4 else x)83re5 = re5[re5.Day.astype(int) < 32]84re5 = re5[re5.Month.astype(int) < 13]85df5 = pd.DataFrame((re5['Month']+'/'+re5['Day']+'/'+re5['Year']).astype('datetime64'), columns = ['Date'])86df5.reset_index(inplace = True)87df5.drop(['match'], axis = 1, inplace = True)88df5 = df5.rename(columns={'level_0': 'Old_Index', 'Date': 'Date'})89df5.head()90# In[12]:91re6 = df.str.extractall(r'(?:(?P<Month>\d{1,2})[/](?P<Day>)(?P<Year>(?:19|20)?\d{4}))')92# In[13]:93re6['Month'] = re6['Month'].str.replace(".", "").str.strip()94re6['Month'] = re6['Month'].str.replace(",", "").str.strip()95re6['Month'] = re6['Month'].apply(lambda x: '0'+x if len(x) < 2 else x)96re6['Day'] = re6['Day'].replace(np.nan, '01', regex = True)97re6['Year'] = re6['Year'].apply(lambda x: '19'+x if len(x) < 4 else x)98re6 = re6[re6.Day.astype(int) < 32]99re6 = re6[re6.Month.astype(int) < 13]100df6 = pd.DataFrame((re6['Month']+'/'+re6['Day']+'/'+re6['Year']).astype('datetime64'), columns = ['Date'])101df6.reset_index(inplace = True)102df6.drop(['match'], axis = 1, inplace = True)103df6 = df6.rename(columns={'level_0': 'Old_Index', 'Date': 'Date'})104df6.head()105# In[14]:106re7 = df.str.extractall(r'(?:(?P<Month>)(?P<Day>)(?P<Year>(?:19|20)\d{2}))')107# In[15]:108re7['Month'] = re7['Month'].replace(np.nan, '01', regex = True)109re7['Day'] = re7['Day'].replace(np.nan, '01', regex = True)110re7['Year'] = re7['Year'].apply(lambda x: '19'+x if len(x) < 4 else x)111df7 = pd.DataFrame((re7['Month']+'/'+re7['Day']+'/'+re7['Year']).astype('datetime64'), columns = ['Date'])112df7.reset_index(inplace = True)113df7.drop(['match'], axis = 1, inplace = True)114df7 = df7.rename(columns={'level_0': 'Old_Index', 'Date': 'Date'})115df7.head()116# # Merge DataFrames117# In[16]:118final_df = pd.concat([df1, df2, df3, df4])119final_df = final_df.sort_values('Old_Index')120final_df.reset_index(drop = True, inplace = True)121final_df.head()122# In[17]:123merge_df = pd.merge(final_df, df5, on = 'Old_Index', how = 'outer', validate = 'one_to_one')124merge_df.Date_x.fillna(merge_df.Date_y, inplace = True)125merge_df.drop(['Date_y'], axis = 1, inplace = True)126merge_df.rename(columns = {'Date_x': 'Date'}, inplace = True)127final_df = merge_df128final_df.head()129# In[18]:130merge_df = pd.merge(final_df, df6, on = 'Old_Index', how = 'outer', validate = 'one_to_one')131merge_df.Date_x.fillna(merge_df.Date_y, inplace = True)132merge_df.drop(['Date_y'], axis = 1, inplace = True)133merge_df.rename(columns = {'Date_x': 'Date'}, inplace = True)134final_df = merge_df135final_df.head()136# In[19]:137merge_df = pd.merge(final_df, df7, on = 'Old_Index', how = 'outer', validate = 'one_to_one')138merge_df.Date_x.fillna(merge_df.Date_y, inplace = True)139merge_df.drop(['Date_y'], axis = 1, inplace = True)140merge_df.rename(columns = {'Date_x': 'Date'}, inplace = True)141final_df = merge_df142final_df.head()143# In[20]:144final_df = final_df.sort_values('Date')145# In[21]:146S1 = pd.Series(list(final_df['Old_Index']))147# In[22]:148S1149# In[23]:150# flat_list = [item for sublist in re1 for item in sublist]151# df1 = pd.DataFrame(flat_list, columns = ['Dates'])152# merge_df.query('Date_x == "NaT"')153# In[24]:154# final_df['Date'] = pd.to_datetime(final_df['Date'])155final_df.sort_values('Date')156# final_df157# In[25]:158re4159# In[26]:160# Your code here161# Full date162global df163dates_extracted = df.str.extractall(r'(?P<origin>(?P<month>\d?\d)[/|-](?P<day>\d?\d)[/|-](?P<year>\d{4}))')164index_left = ~df.index.isin([x[0] for x in dates_extracted.index])165dates_extracted = dates_extracted.append(df[index_left].str.extractall(r'(?P<origin>(?P<month>\d?\d)[/|-](?P<day>([0-2]?[0-9])|([3][01]))[/|-](?P<year>\d{2}))'))166index_left = ~df.index.isin([x[0] for x in dates_extracted.index])167del dates_extracted[3]168del dates_extracted[4]169dates_extracted = dates_extracted.append(df[index_left].str.extractall(r'(?P<origin>(?P<day>\d?\d) ?(?P<month>[a-zA-Z]{3,})\.?,? (?P<year>\d{4}))'))170index_left = ~df.index.isin([x[0] for x in dates_extracted.index])171dates_extracted = dates_extracted.append(df[index_left].str.extractall(r'(?P<origin>(?P<month>[a-zA-Z]{3,})\.?-? ?(?P<day>\d\d?)(th|nd|st)?,?-? ?(?P<year>\d{4}))'))172del dates_extracted[3]173index_left = ~df.index.isin([x[0] for x in dates_extracted.index])174# Without day175dates_without_day = df[index_left].str.extractall('(?P<origin>(?P<month>[A-Z][a-z]{2,}),?\.? (?P<year>\d{4}))')176dates_without_day = dates_without_day.append(df[index_left].str.extractall(r'(?P<origin>(?P<month>\d\d?)/(?P<year>\d{4}))'))177dates_without_day['day'] = 1178dates_extracted = dates_extracted.append(dates_without_day)179index_left = ~df.index.isin([x[0] for x in dates_extracted.index])180# Only year181dates_only_year = df[index_left].str.extractall(r'(?P<origin>(?P<year>\d{4}))')182dates_only_year['day'] = 1183dates_only_year['month'] = 1184dates_extracted = dates_extracted.append(dates_only_year)185index_left = ~df.index.isin([x[0] for x in dates_extracted.index])186# Year187dates_extracted['year'] = dates_extracted['year'].apply(lambda x: '19' + x if len(x) == 2 else x)188dates_extracted['year'] = dates_extracted['year'].apply(lambda x: str(x))189# Month190dates_extracted['month'] = dates_extracted['month'].apply(lambda x: x[1:] if type(x) is str and x.startswith('0') else x)191month_dict = dict({'September': 9, 'Mar': 3, 'November': 11, 'Jul': 7, 'January': 1, 'December': 12,192                   'Feb': 2, 'May': 5, 'Aug': 8, 'Jun': 6, 'Sep': 9, 'Oct': 10, 'June': 6, 'March': 3,193                   'February': 2, 'Dec': 12, 'Apr': 4, 'Jan': 1, 'Janaury': 1,'August': 8, 'October': 10,194                   'July': 7, 'Since': 1, 'Nov': 11, 'April': 4, 'Decemeber': 12, 'Age': 8})195dates_extracted.replace({"month": month_dict}, inplace=True)...

test_all.py

Source:test_all.py

...19    d = tempfile.mkdtemp(prefix="pyunpack_test_")20    return d21def test():22    with pytest.raises(ValueError):23        Archive("blabla").extractall(tempfile.gettempdir())24    with pytest.raises(PatoolError):25        Archive(__file__).extractall(tempfile.gettempdir())26def create_arc(format):27    d = tmpdir()28    x_txt = join(d, "x.txt")29    open(x_txt, "w").write("123")30    # x_zip = d / "x.zip"31    os.chdir(d)32    x_zip = make_archive(33        "x",34        format,  # the archive format - or tar, bztar, gztar35        root_dir=None,  # root for archive - current working dir if None36        base_dir=None,37    )  # start archiving from here - cwd if None too38    # EasyProcess(["zip", "--no-dir-entries", x_zip, "x.txt"], cwd=d).call()39    return x_zip40def test2():41    for f in formats:42        print(f)43        x_zip = create_arc(f)44        with pytest.raises(ValueError):45            Archive(x_zip).extractall("blabla")46        d = tmpdir()47        Archive(x_zip, backend="patool").extractall(d)48        ok_file(d, "x.txt")49        d = tmpdir()50        Archive(x_zip).extractall(d)51        ok_file(d, "x.txt")52        d = tmpdir()53        Archive(x_zip, backend="auto").extractall(d)54        ok_file(d, "x.txt")55        if f == "zip":56            d = tmpdir()57            Archive(x_zip, backend="zipfile").extractall(d)58            ok_file(d, "x.txt")59        d = tmpdir()60        cli.extractall(x_zip, d)61        ok_file(d, "x.txt")62def test_subdir():63    for f in formats:64        x_zip = create_arc(f)65        d = join(tmpdir(), "subdir")66        with pytest.raises(ValueError):67            Archive(x_zip).extractall(d, auto_create_dir=False)68        d = join(tmpdir(), "subdir")69        Archive(x_zip, backend="auto").extractall(d, auto_create_dir=True)...

Playwright tutorial

LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.

Chapters:

What is Playwright : Playwright is comparatively new but has gained good popularity. Get to know some history of the Playwright with some interesting facts connected with it.
How To Install Playwright : Learn in detail about what basic configuration and dependencies are required for installing Playwright and run a test. Get a step-by-step direction for installing the Playwright automation framework.
Playwright Futuristic Features: Launched in 2020, Playwright gained huge popularity quickly because of some obliging features such as Playwright Test Generator and Inspector, Playwright Reporter, Playwright auto-waiting mechanism and etc. Read up on those features to master Playwright testing.
What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
Handling Alerts and Dropdowns in Playwright : Playwright interact with different types of alerts and pop-ups, such as simple, confirmation, and prompt, and different types of dropdowns, such as single selector and multi-selector get your hands-on with handling alerts and dropdown in Playright testing.
Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.