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home.py
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home.py
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import streamlit as st
from streamlit import caching
import SessionState
import pandas as pd
from pandas_profiling import ProfileReport
from streamlit_pandas_profiling import st_profile_report
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from gaugeChart import gauge
import numpy as np
import datetime
from dateutil.relativedelta import relativedelta
from string_grouper import match_strings
import re
from TRnoChecker import isValidTCID, taxnum_checker
import TR_name_gender
from downloader import get_table_download_link
from utils import download_button
st.set_option('deprecation.showPyplotGlobalUse', False)
st.set_page_config(page_title="Your Data Quality Detector", page_icon="馃攳", layout="wide")
hide_streamlit_style = """
<style>
footer {
visibility: hidden;
}
footer:after {
content:'developed by Beytullah Ali G枚yem';
visibility: visible;
display: block;
position: relative;
#background-color: red;
padding: 5px;
top: 2px;
}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
@st.cache(allow_output_mutation=True, persist = True)
def beforeSTable():
global before
before = pd.DataFrame(columns=["Column", "Null Records", "Out of Format Records", "Proper Format Records", "Column DQ Score(%)"])
return before
@st.cache(allow_output_mutation=True, persist = True)
def afterSTable():
global after
after = pd.DataFrame(columns=["Column", "Null Records", "Out of Format Records", "Proper Format Records", "Column DQ Score(%)"])
return after
@st.cache(allow_output_mutation=True, persist=True)
def reading_dataset():
global dataset
try:
dataset = pd.read_excel(uploaded_file)
except ValueError:
dataset = pd.read_csv(uploaded_file)
return dataset
st.image('DataQtor.png', width=250)
with st.sidebar.subheader('Upload your file'):
uploaded_file = st.sidebar.file_uploader("Please upload a file of type: xlsx, csv", type=["xlsx", "csv"])
st.sidebar.subheader("Notepad")
st.sidebar.text_area(label="Enter your note here!", value="You don't have any notes", height=30)
st.sidebar.subheader("")
st.sidebar.write(" [](https://www.buymeacoffee.com/baligoyem) [](https://tr.linkedin.com/in/beytullah-ali-g%C3%B6yem-461749152)")
st.sidebar.subheader("")
if st.sidebar.button("Clear Cache"):
caching.clear_memo_cache()
st.sidebar.success("Cache is cleared!")
if uploaded_file is not None:
before = beforeSTable()
after = afterSTable()
dataset = reading_dataset()
task = st.selectbox("Menu", ["Data Profiler", "Data Quality Detector",
"Data Corrector", "Review Summary Report and Download Adjusted Data", "Contact Me"])
st.session_state.beforeSS = before
st.session_state.afterSS = after
if task == "Data Profiler":
pr = ProfileReport(dataset, explorative=True, orange_mode=True)
st_profile_report(pr)
elif task == "Data Quality Detector":
numerical = dataset.select_dtypes(include=['number', 'bool', 'datetime64[ns]', 'timedelta64'])
st.table(pd.DataFrame([[dataset.shape[0], dataset.shape[1],
(dataset.shape[1] - numerical.shape[1]), numerical.shape[1]]],
columns=["Row Count", "Column Count", "Nominal Column Count",
"Numeric Column Count"], index=[""]))
columns = dataset.columns.to_numpy().tolist()
useless = dataset[dataset.isnull().sum(axis=1) > (dataset.shape[1] / 2)]
uselessRows_count = useless.shape[0]
if uselessRows_count > 0:
st.write(str(uselessRows_count), "rows may be useless:", useless)
st.write("")
duplicated = dataset[dataset.duplicated()]
idx_dup = dataset[dataset.duplicated()].index
duplicatedRows_count = duplicated.shape[0]
if duplicatedRows_count == 0:
st.success("There is no duplicated rows in the dataset.")
else:
st.write("There are", str(duplicatedRows_count), "duplicated rows in the dataset:",
dataset[dataset.duplicated()])
if st.button("Drop Duplicated Rows"):
dataset.drop(index=idx_dup, inplace=True)
st.success("Duplicated rows were deleted.")
st.write("---")
if st.checkbox("Run Column Detector", key="run_col_detector"):
cols = st.beta_columns(2)
with cols[0]:
selected_column = st.selectbox("Column", columns, key="col_select")
with cols[1]:
label = st.selectbox("Define the DQ Rule",
["Select", "Define the DQ Rule Yourself", "E-mail Address", "艦ehir (TR)",
"T.C. Kimlik No", "Telefon No", "Vergi Kimlik No"], key="define_dq_rule")
cols = st.beta_columns((7, 1.4, 1.6))
with cols[0]:
st.write("Type:", dataset[selected_column].dtype)
if (dataset[selected_column].dtype == np.int16 or dataset[selected_column].dtype == np.int32 or
dataset[selected_column].dtype == np.int64 or dataset[selected_column].dtype == np.float16 or dataset[
selected_column].dtype == np.float32 or dataset[selected_column].dtype == np.float64):
with cols[1]:
if st.checkbox("Show Total"):
total = dataset[selected_column].sum()
with cols[2]:
st.write(": ", '{:,.2f}'.format(total))
elif dataset[selected_column].dtype == 'object':
strMinLength = dataset[selected_column].str.len().min()
strMaxLength = dataset[selected_column].str.len().max()
valueStrMin = dataset.reset_index(drop=True).loc[dataset[selected_column].str.len().argmin(), selected_column]
valueStrMax = dataset.reset_index(drop=True).loc[dataset[selected_column].str.len().argmax(), selected_column]
valueMin = dataset[selected_column].dropna().astype(str).sort_values().min()
valueMax = dataset[selected_column].dropna().astype(str).sort_values().max()
strCA = pd.DataFrame(
[[strMinLength, valueStrMin, strMaxLength, valueStrMax, valueMin, valueMax]],
columns=["Min Length", "Value (minLen)", "Max Length", "Value (maxLen)",
"Min (Alphabetic)", "Max (Alphabetic)"], index=["info"])
st.write(strCA)
filledCount = dataset[selected_column].count()
nanCount = int(dataset[selected_column].isna().sum())
nonNullValues_per = (dataset[selected_column].count() / len(dataset) * 100).round(1)
nullValues_per = (dataset[selected_column].isna().sum() / len(dataset) * 100).round(1)
nanDF = pd.DataFrame([[filledCount, nonNullValues_per],
[nanCount, nullValues_per]], columns=["count", "percentage(%)"],
index=["non-NaN", "isNaN"])
def color_survived(val):
if val == nanDF["percentage(%)"]["isNaN"] and val >= 50:
color = '#FA8072'
return f'background-color: {color}'
else:
color = 'white'
return f'background-color: {color}'
cols = st.beta_columns((3, 7))
with cols[0]:
st.write(
nanDF.style.format({'count': '{:,}', 'percentage(%)': '{:.1f}'}).applymap(color_survived, subset=['percentage(%)']))
with cols[1]:
describe = dataset[selected_column].describe().to_frame().T
st.write(describe.style.format(
{"count": '{:,.0f}', "mean": '{:,.1f}', "std": '{:,.1f}', "min": '{:,.2f}',
"25%": '{:,.2f}', "50%": '{:,.2f}', "75%": '{:,.2f}', "max": '{:,.2f}'}))
freqCount = dataset[selected_column].value_counts().to_frame(name="count")
per = (dataset[selected_column].value_counts(normalize=True).round(3) * 100).to_frame(
name="percentage(%)")
general = pd.concat([freqCount, per], axis=1)
if general.shape[0] > 10:
cols = st.beta_columns(3)
with cols[0]:
st.write("5 most frequent values in this column:",
general.iloc[np.r_[0:5]].style.format(
{"count": '{:,}', 'percentage(%)': '{:.1f}'}))
with cols[1]:
st.write("5 least frequent values in this column:",
general.iloc[np.r_[-5:0]].style.format(
{'count': '{:,}', 'percentage(%)': '{:.1f}'}))
with cols[2]:
if st.checkbox("Frequency Table", key="freq_table"):
st.write(general.style.format({'count': '{:,}', 'percentage(%)': '{:.1f}'}))
else:
st.write("Frequency Table:", general.style.format({'count': '{:,}', 'percentage(%)': '{:.1f}'}))
if dataset[selected_column].value_counts().count() > 10:
unexpected = general[general["percentage(%)"] < 0.1]
if not unexpected.empty:
unexpected['value'] = unexpected.index.astype('str')
if (unexpected["value"].value_counts().count() <= 25):
fig = plt.figure(figsize=(20, 6))
ax = fig.add_axes([0, 0, 1, 1])
plt.title("Unexpected Value Graph\n ", size=20, loc="left")
ax.bar(unexpected.value, unexpected["count"], color="#262730")
ax = plt.gca()
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.tick_params(axis='both', which='major', labelsize=14)
plt.xticks(rotation=45)
plt.xlabel("value", fontsize=15)
plt.ylabel("count", fontsize=15)
plt.show()
st.pyplot(fig)
if (dataset[selected_column].dtype == np.int16 or dataset[selected_column].dtype == np.int32 or
dataset[selected_column].dtype == np.int64 or dataset[selected_column].dtype == np.float16 or dataset[
selected_column].dtype == np.float32 or dataset[selected_column].dtype == np.float64):
cols = st.beta_columns(2)
with cols[0]:
if st.checkbox("Show p-0-n"):
countp = dataset[dataset[selected_column] > 0][selected_column].count()
count0 = dataset[dataset[selected_column] == 0][selected_column].count()
countn = dataset[dataset[selected_column] < 0][selected_column].count()
countp_per = (countp / len(dataset) * 100).round(1)
count0_per = (count0 / len(dataset) * 100).round(1)
countn_per = (countn / len(dataset) * 100).round(1)
dfp0n = pd.DataFrame([[countp, countp_per],
[count0, count0_per],
[countn, countn_per]], columns=["count", "percentage(%)"],
index=["positive value", "0-value", "negative value"])
st.write(dfp0n.style.format({'count': '{:,}', 'percentage(%)': '{:.1f}'}))
dataset.pattern = dataset[selected_column].astype(str).replace('nan', np.NaN).str.replace(
'[A-Za-z脰脟臑陌艦脺枚莽臒谋艧眉]', 'A').dropna()
dataset.pattern = dataset.pattern.astype(str).str.replace('[0-9]', "9")
countpattern = dataset.pattern.value_counts().to_frame(name="count")
perforPattern = (dataset.pattern.value_counts(normalize=True).round(3) * 100).to_frame(
name="percentage(%)")
generalPattern = pd.concat([countpattern, perforPattern], axis=1)
if generalPattern.shape[0] > 10:
cols = st.beta_columns(3)
with cols[0]:
st.write("5 most frequent patterns in this column:",
generalPattern.iloc[np.r_[0:5]].style.format(
{"count": '{:,}', 'percentage(%)': '{:.1f}'}))
with cols[1]:
st.write("5 least frequent patterns in this column:",
generalPattern.iloc[np.r_[-5:0]].style.format(
{'count': '{:,}', 'percentage(%)': '{:.1f}'}))
with cols[2]:
if st.checkbox("Pattern Table", key="pattern_table"):
st.write(generalPattern.style.format({'count': '{:,}', 'percentage(%)': '{:.1f}'}))
else:
st.write("Pattern Table:",
generalPattern.style.format({'count': '{:,}', 'percentage(%)': '{:.1f}'}))
st.warning(
"Duplicated Data Detector works well especially while checking Customer-related datasets. If you're going to run 'DDD', make sure the column is fit for purpose!")
if st.checkbox("Run Duplicated Data Detector"):
try:
st.write("Records that may be duplicate:",
pd.concat(g for _, g in dataset.groupby(selected_column) if len(g) > 1))
except ValueError:
if dataset[selected_column].isnull().sum() == dataset.shape[0]:
st.info("All values in this column are NaN.")
else:
st.info("All values in this column are unique.")
if st.checkbox("Run Similarity Detector"):
matches = match_strings(dataset[selected_column].apply(str), min_similarity=0.52)
# Look at only the non-exact matches:
st.write(matches[matches['left_{0}'.format(selected_column)] != matches[
'right_{0}'.format(selected_column)]].sort_values(['similarity'],
ascending=False).head(50))
NOFR = 0
if label == "Define the DQ Rule Yourself":
st.write("---")
st.subheader("Define the DQ Rule")
st.write("")
if (dataset[selected_column].dtype == np.int16 or dataset[selected_column].dtype == np.int32 or
dataset[selected_column].dtype == np.int64):
editlen = st.selectbox("Select Qualification",
["Equals", "Greater than or equal to", "Less than", "Between"])
if editlen == "Equals":
length = st.number_input("Value", format="%i", value=0, min_value=-5000000000,
max_value=5000000000, step=1)
f1 = dataset[selected_column][~(dataset[selected_column] == length)]
elif editlen == "Greater than or equal to":
length = st.number_input("Value", format="%i", value=0, min_value=-5000000000,
max_value=5000000000, step=1)
f1 = dataset[selected_column][~(dataset[selected_column] >= length)]
elif editlen == "Less than":
length = st.number_input("Value", format="%i", value=0, min_value=-5000000000,
max_value=5000000000, step=1)
f1 = dataset[selected_column][~(dataset[selected_column] < length)]
elif editlen == "Between":
cols = st.beta_columns((2, 1, 1, 1, 2))
with cols[0]:
length1 = st.number_input("Value1", format="%i", value=0, min_value=-5000000000,
max_value=5000000000, step=1)
with cols[2]:
st.write("AND")
with cols[4]:
length2 = st.number_input("Value2", format="%i", value=0, min_value=-5000000000,
max_value=5000000000, step=1)
cols = st.beta_columns((1, 1, 1))
with cols[1]:
f1 = dataset[selected_column][~((dataset[selected_column] >= length1) & (
dataset[selected_column] <= length2))]
notuseful = f1.drop_duplicates(keep='first').dropna()
st.write(str(
"Values that do not match with the quality rules defined for " + selected_column + ":"),
notuseful)
NOFR = f1.shape[0]
elif (dataset[selected_column].dtype == np.float16 or dataset[
selected_column].dtype == np.float32 or dataset[selected_column].dtype == np.float64):
editlen = st.selectbox("Select Qualification",
["Equals", "Greater than or equal to", "Less than", "Between"])
if editlen == "Equals":
length = st.number_input("Value", format="%f", value=0.0, min_value=-5000000000.0,
max_value=5000000000.0, step=0.01)
f1 = dataset[selected_column][~(dataset[selected_column] == length)]
elif editlen == "Greater than or equal to":
length = st.number_input("Value", format="%f", value=0.0, min_value=-5000000000.0,
max_value=5000000000.0, step=0.01)
f1 = dataset[selected_column][~(dataset[selected_column] >= length)]
elif editlen == "Less than":
length = st.number_input("Value", format="%f", value=0.0, min_value=-5000000000.0,
max_value=5000000000.0, step=0.01)
f1 = dataset[selected_column][~(dataset[selected_column] < length)]
elif editlen == "Between":
cols = st.beta_columns((2, 1, 1, 1, 2))
with cols[0]:
length1 = st.number_input("Value1", format="%f", value=0.0, min_value=-5000000000.0,
max_value=5000000000.0, step=0.01)
with cols[2]:
st.write("AND")
with cols[4]:
length2 = st.number_input("Value2", format="%f", value=0.0, min_value=-5000000000.0,
max_value=5000000000.0, step=0.01)
cols = st.beta_columns((1, 1, 1))
with cols[1]:
f1 = dataset[selected_column][~((dataset[selected_column] >= length1) & (
dataset[selected_column] <= length2))]
notuseful = f1.drop_duplicates(keep='first').dropna()
st.write(str(
"Values that do not match with the quality rules defined for " + selected_column + ":"),
notuseful)
NOFR = f1.shape[0]
elif dataset[selected_column].dtype.name == 'datetime64[ns]':
editlen = st.selectbox("Select Qualification",
["Equals", "Greater than or equal to", "Less than", "Between"])
if editlen == "Equals":
date = st.date_input("Value", value=datetime.date.today(), min_value=datetime.date.today()-relativedelta(years=125), max_value=datetime.date.today()+relativedelta(years=35))
f1 = dataset[selected_column][~(dataset[selected_column] == pd.to_datetime(date))]
elif editlen == "Greater than or equal to":
date = st.date_input("Value", value=datetime.date.today(), min_value=datetime.date.today()-relativedelta(years=125), max_value=datetime.date.today()+relativedelta(years=35))
f1 = dataset[selected_column][~(dataset[selected_column] >= pd.to_datetime(date))]
elif editlen == "Less than":
date = st.date_input("Value", value=datetime.date.today(), min_value=datetime.date.today()-relativedelta(years=125), max_value=datetime.date.today()+relativedelta(years=35))
f1 = dataset[selected_column][~(dataset[selected_column] < pd.to_datetime(date))]
elif editlen == "Between":
cols = st.beta_columns((2, 1, 1, 1, 2))
with cols[0]:
date1 = st.date_input("Value1", value=datetime.date.today(), min_value=datetime.date.today()-relativedelta(years=125), max_value=datetime.date.today()+relativedelta(years=35))
with cols[2]:
st.write("AND")
with cols[4]:
date2 = st.date_input("Value2", value=datetime.date.today(), min_value=datetime.date.today()-relativedelta(years=125), max_value=datetime.date.today()+relativedelta(years=35))
cols = st.beta_columns((1, 1, 1))
with cols[1]:
f1 = dataset[selected_column][~((dataset[selected_column] >= pd.to_datetime(date1)) & (
dataset[selected_column] <= pd.to_datetime(date2)))]
notuseful = f1.drop_duplicates(keep='first').dropna()
st.write(str(
"Values that do not match with the quality rules defined for " + selected_column + ":"),
notuseful)
NOFR = f1.shape[0]
else:
dataset[selected_column] = dataset[selected_column].astype(str).replace('nan', np.NaN)
f1 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 9999)]
if st.checkbox("Set Qualification by Length"):
editlen = st.selectbox("Select Qualification",
["Equals", "Greater than or equal to", "Less than", "Between"])
if editlen == "Equals":
length = st.number_input("Value", format="%i", value=0, min_value=0, max_value=1000,
step=1)
f1 = dataset[selected_column][
~(dataset[selected_column].astype(str).map(len) == length)]
elif editlen == "Greater than or equal to":
length = st.number_input("Value", format="%i", value=0, min_value=0, max_value=1000,
step=1)
f1 = dataset[selected_column][
~(dataset[selected_column].astype(str).map(len) >= length)]
elif editlen == "Less than":
length = st.number_input("Value", format="%i", value=0, min_value=0, max_value=1000,
step=1)
f1 = dataset[selected_column][
~(dataset[selected_column].astype(str).map(len) < length)]
elif editlen == "Between":
cols = st.beta_columns((2, 1, 1, 1, 2))
with cols[0]:
length1 = st.number_input("Value1", format="%i", value=0, min_value=0,
max_value=1000, step=1)
with cols[2]:
st.write("AND")
with cols[4]:
length2 = st.number_input("Value2", format="%i", value=0, min_value=0,
max_value=1000, step=1)
cols = st.beta_columns((1, 1, 1))
with cols[1]:
f1 = dataset[selected_column][~(
(dataset[selected_column].astype(str).map(len) >= length1) & (
dataset[selected_column].astype(str).map(len) <= length2))]
f2 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 9999)]
rule2 = st.checkbox("Cannot contain special characters")
if rule2:
SPEC_CHARS = ["@", "-", "_", ".", ",", "~", "`", "!", "#", "$", "%", "^", "&", "*", "(",
")", "+", "=", "{", "}", "[", "]", "|", "/", ":", ";", '"', "'", "<", ">",
"?"]
pat = '|'.join(['({})'.format(re.escape(c)) for c in SPEC_CHARS])
f2 = dataset[selected_column][dataset[selected_column].str.contains(pat, na=False)]
rule2in = st.multiselect("exclusive", SPEC_CHARS)
if rule2in:
pat = '|'.join(['({})'.format(re.escape(c)) for c in rule2in])
f2 = dataset[selected_column][
(~dataset[selected_column].str.contains(pat, na=False)) & (
dataset[selected_column].str.contains(
'|'.join(['({})'.format(re.escape(c)) for c in SPEC_CHARS]), na=False))]
f3 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 9999)]
rule3 = st.checkbox("Cannot contain numbers")
if rule3:
NUM_CHARS = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
pat = '|'.join(['({})'.format(re.escape(c)) for c in NUM_CHARS])
f3 = dataset[selected_column][dataset[selected_column].str.contains(pat, na=False)]
f4 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 9999)]
rule4 = st.checkbox("Cannot contain spaces")
if rule4:
f4 = dataset[selected_column][dataset[selected_column].str.contains(' ', na=False)]
f5 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 9999)]
rule5 = st.checkbox("Specified values cannot be entered")
if rule5:
canContain = st.text_input(
"Please separate the values with commas, for example 'bilinmiyor,yok,xxxxx' (Do not put a space after comma)")
canContain = canContain.split(',')
f5 = dataset[selected_column][dataset[selected_column].isin(canContain)]
notuseful = (pd.concat([f1, f2, f3, f4, f5], ignore_index=False, sort=False)).replace('nan', np.NaN).drop_duplicates(keep='first').dropna()
st.write(str(
"Values that do not match with the quality rules defined for " + selected_column + ":"),
notuseful)
notuseful = pd.concat([f1, f2, f3, f4, f5], ignore_index=False, sort=False).replace('nan',
np.NaN).dropna()
notuseful = notuseful.groupby(notuseful.index).first()
NOFR = notuseful.shape[0]
if label == "E-mail Address":
f1 = dataset[selected_column][dataset[selected_column].str.contains("~", na=False) | dataset[
selected_column].str.contains("`", na=False) | dataset[selected_column].str.contains("!",
na=False) |
dataset[selected_column].str.contains("#", na=False) | dataset[
selected_column].str.contains("%", na=False) | dataset[
selected_column].str.contains("&", na=False) | dataset[
selected_column].str.contains("=", na=False) | dataset[
selected_column].str.contains("{", na=False) | dataset[
selected_column].str.contains("}", na=False) | dataset[
selected_column].str.contains("]", na=False) | dataset[
selected_column].str.contains(":", na=False) | dataset[
selected_column].str.contains(";", na=False) | dataset[
selected_column].str.contains("/", na=False) | dataset[
selected_column].str.contains(">", na=False) | dataset[
selected_column].str.contains("<", na=False) | dataset[
selected_column].str.contains("'", na=False) | dataset[
selected_column].str.contains('"', na=False) | dataset[
selected_column].str.contains(' ', na=False) | dataset[
selected_column].str.contains('\(', na=False) | dataset[
selected_column].str.contains('\\\\', na=False) | dataset[
selected_column].str.contains(',', na=False) | dataset[
selected_column].str.contains('\?', na=False) | dataset[
selected_column].str.contains('\|', na=False) | dataset[
selected_column].str.contains('\[', na=False) | dataset[
selected_column].str.contains('\+', na=False) | dataset[
selected_column].str.contains('\)', na=False) | dataset[
selected_column].str.contains('\*', na=False) | dataset[
selected_column].str.contains('\^', na=False) | dataset[
selected_column].str.contains('\$', na=False)]
f2 = dataset[selected_column][dataset[selected_column].astype(str).map(len) <= 6]
f3 = dataset[selected_column][~dataset[selected_column].str.contains("@", na=False) | ~dataset[
selected_column].str.contains(".", na=False)]
f4 = dataset[selected_column][dataset[selected_column].str.count("@") > 1]
f5 = dataset[selected_column][dataset[selected_column].str.startswith("@", na=False) | dataset[
selected_column].str.startswith("yok@", na=False) | dataset[selected_column].str.startswith(
"YOK@", na=False) | dataset[selected_column].str.startswith("www", na=False) | dataset[
selected_column].str.startswith("bbb@", na=False) | dataset[
selected_column].str.startswith("girilecek@", na=False) |
dataset[selected_column].str.startswith("deneme@", na=False) |
dataset[selected_column].str.startswith("Mailadresi@", na=False) |
dataset[selected_column].str.startswith("dummy@", na=False)]
searchfor = ["\@\.", "\.\@", "\@-"]
f6 = dataset[selected_column][
dataset[selected_column].str.contains('|'.join(searchfor), na=False)]
v1 = dataset[selected_column][dataset[selected_column].str.contains("谋", na=False) | dataset[
selected_column].str.contains("陌", na=False) | dataset[selected_column].str.contains("莽",
na=False) |
dataset[selected_column].str.contains("脟", na=False) | dataset[
selected_column].str.contains("艧", na=False) | dataset[
selected_column].str.contains("艦", na=False) | dataset[
selected_column].str.contains("臒", na=False) | dataset[
selected_column].str.contains("臑", na=False) | dataset[
selected_column].str.contains("眉", na=False) | dataset[
selected_column].str.contains("脺", na=False) | dataset[
selected_column].str.contains("枚", na=False) | dataset[
selected_column].str.contains("脰", na=False)]
v2 = dataset[selected_column][dataset[selected_column].str.contains(r'[A-Z]', na=False)]
v3 = dataset[selected_column][
dataset[selected_column].str.endswith("gmail", na=False) | dataset[
selected_column].str.endswith("yahoo", na=False) | dataset[
selected_column].str.endswith("hotmail", na=False) | dataset[
selected_column].str.endswith("msn", na=False) | dataset[selected_column].str.endswith(
"@live", na=False) | dataset[selected_column].str.endswith("yandex", na=False) |
dataset[selected_column].str.endswith("outlook", na=False) | dataset[
selected_column].str.endswith("windowslive", na=False) | dataset[
selected_column].str.endswith(".com.t", na=False) | dataset[
selected_column].str.endswith(".o", na=False) | dataset[selected_column].str.endswith(
".cm", na=False) | dataset[selected_column].str.endswith(".co", na=False) | dataset[
selected_column].str.endswith(".ocom", na=False) | dataset[
selected_column].str.endswith(".ney", na=False) | dataset[selected_column].str.endswith(
".co.", na=False) | dataset[selected_column].str.endswith(".cvom", na=False) | dataset[
selected_column].str.endswith(".comtr", na=False) | dataset[
selected_column].str.endswith(".com.", na=False) | dataset[
selected_column].str.endswith(".comom", na=False) | dataset[
selected_column].str.startswith("ingo@", na=False) | dataset[
selected_column].str.endswith(".c", na=False) | dataset[selected_column].str.endswith(
".r", na=False) | dataset[selected_column].str.endswith(".com.tr'", na=False) | dataset[
selected_column].str.endswith(".com.tr/", na=False)]
searchfordomain = ["windowlive", "hotmil", "hatmail", "hotmial", "gamil", "gmmail", "outlok",
"yaaho"]
v4 = dataset[selected_column][
dataset[selected_column].str.contains('|'.join(searchfordomain), na=False)]
notuseful = (pd.concat([f1, f2, f3, f4, f5, f6, v1, v2, v3, v4], ignore_index=False,
sort=False).drop_duplicates(keep='last')).dropna()
NOFR = notuseful.shape[0]
st.write("Records that do not match with the quality rules defined for E-mail Address:",
notuseful)
if label == "T.C. Kimlik No":
notuseful = dataset[selected_column][
dataset[selected_column].apply(isValidTCID) == False].dropna()
NOFR = notuseful.shape[0]
st.write("Records that do not match with the quality rules defined for T.C. Kimlik No:",
notuseful)
if label == "Vergi Kimlik No":
notuseful = dataset[selected_column][
dataset[selected_column].apply(taxnum_checker) == False].dropna()
NOFR = notuseful.shape[0]
st.write("Records that do not match with the quality rules defined for Vergi Kimlik No:",
notuseful)
if label == "艦ehir (TR)":
sehirler = ["Adana", "Ad谋yaman", "Afyon", "A臒r谋", "Amasya", "Ankara", "Antalya",
"Artvin", "Ayd谋n",
"Bal谋kesir", "Bilecik", "Bing枚l", "Bitlis", "Bolu", "Burdur", "Bursa", "脟anakkale",
"脟ank谋r谋",
"脟orum", "Denizli", "Diyarbak谋r", "Edirne", "Elaz谋臒", "Erzincan", "Erzurum",
"Eski艧ehir",
"Gaziantep", "Giresun", "G眉m眉艧hane", "Hakkari", "Hatay", "Isparta", "陌莽el",
"陌stanbul", "陌stanbul-Avrupa", "陌stanbul-Anadolu",
"陌zmir", "Kars", "Kastamonu", "Kayseri", "K谋rklareli", "K谋r艧ehir", "Kocaeli",
"Konya",
"K眉tahya", "Malatya", "Manisa", "Kahramanmara艧", "Mardin", "Mu臒la", "Mu艧",
"Nev艧ehir", "Ni臒de",
"Ordu", "Rize", "Sakarya", "Samsun", "Siirt", "Sinop", "Sivas", "Tekirda臒", "Tokat",
"Trabzon",
"Tunceli", "艦anl谋urfa", "U艧ak", "Van", "Yozgat", "Zonguldak", "Aksaray", "Bayburt",
"Karaman",
"K谋r谋kkale", "Batman", "艦谋rnak", "Bart谋n", "Ardahan", "I臒d谋r", "Yalova", "Karab眉k",
"Kilis",
"Osmaniye", "D眉zce"]
notuseful = dataset[selected_column][dataset[selected_column].isin(sehirler) == False].dropna()
NOFR = notuseful.shape[0]
st.write("Records that do not match with the quality rules defined for 艦ehir (TR):", notuseful)
if label == "Telefon No":
dataset[selected_column] = dataset[selected_column].astype(str)
dataset[selected_column] = dataset[selected_column].replace('nan', np.NaN)
d111 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 11) & (
dataset[selected_column].str.startswith("0", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d211 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 11) & (
~dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[3] == " ")]
d311 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 11) & (
~dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[3] == "-")]
d411 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 11) & (
dataset[selected_column].str.startswith("-", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d112 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("90", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d212 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("0-", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d312 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ")]
d412 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str[3] == " ") & (dataset[selected_column].str[7] == " ")]
d512 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str[6] == " ") & (dataset[selected_column].str[9] == " ")]
d612 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == "-") & (
~dataset[selected_column].str.contains(" ", na=False))]
d712 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
~dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[3] == " ") & (
dataset[selected_column].str[8] == "-")]
d812 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("0 ", na=False))]
d912 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[7] == " ")]
d1012 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == "/") & (
~dataset[selected_column].str.contains(" ", na=False))]
d1112 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("-0", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False)) & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("\(", na=False)) & (
~dataset[selected_column].str.contains("\)", na=False))]
d1212 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.contains(" ", na=False)) & (
~dataset[selected_column].str.contains("-", na=False)) & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("\(", na=False)) & (
~dataset[selected_column].str.contains("\)", na=False))]
d1312 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 12) & (
dataset[selected_column].str.startswith("(", na=False)) & (
dataset[selected_column].str[4] == ")") & (
~dataset[selected_column].str.contains("-", na=False)) & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d113 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str[3] == " ") & (
dataset[selected_column].str[7] == " ") & (
dataset[selected_column].str[9] == " ") & (
~dataset[selected_column].str.contains("-", na=False)) & (
~dataset[selected_column].str.contains("/", na=False))]
d213 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str[3] == " ") & (
dataset[selected_column].str[7] == "-") & (
dataset[selected_column].str[10] == "-")]
d313 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("(", na=False)) & (
dataset[selected_column].str[4:6] == ") ")]
d413 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("90", na=False)) & (
dataset[selected_column].str[3] == " ")]
d513 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str[3] == " ") & (
dataset[selected_column].str[7:9] == " ")]
d613 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str[3] == "-") & (
dataset[selected_column].str[7] == "-") & (
dataset[selected_column].str[10] == "-") & (
~dataset[selected_column].str.contains(" ", na=False))]
d713 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[3] == " ") & (
dataset[selected_column].str[8] == " ")]
d813 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4:6] == " ")]
d913 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str[3] == " ") & (
dataset[selected_column].str[7] == " ") & (
dataset[selected_column].str[10] == " ")]
d1013 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ") & (
dataset[selected_column].str[8] == " ")]
d1113 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[7] == " ") & (
dataset[selected_column].str[10] == " ")]
d1213 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("(0", na=False)) & (
dataset[selected_column].str[5] == ")") & (
~dataset[selected_column].str.contains("-", na=False)) & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d1313 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str[3] == "-") & (
dataset[selected_column].str[7] == " ") & (
dataset[selected_column].str[10] == " ") & (
~dataset[selected_column].str.contains("/", na=False))]
d1413 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4:6] == "- ") & (
~dataset[selected_column].str.contains("/", na=False))]
d1513 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str[3:5] == "- ") & (
dataset[selected_column].str[8] == " ") & (
~dataset[selected_column].str.contains("/", na=False))]
d1613 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[7] == " ") & (
dataset[selected_column].str[9] == " ") & (
~dataset[selected_column].str.contains("/", na=False))]
d1713 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0-", na=False)) & (
dataset[selected_column].str[5] == "-") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d1813 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0 ", na=False)) & (
dataset[selected_column].str[5] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1913 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 13) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4:6] == ") ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d114 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d214 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str[3:5] == ") ") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d314 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0 ", na=False)) & (
dataset[selected_column].str[5] == " ") & (
dataset[selected_column].str[9] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d414 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str[3:5] == " ") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d514 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str[3] == " ") & (
dataset[selected_column].str[7] == " ") & (
dataset[selected_column].str[10:12] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d614 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("(", na=False)) & (
dataset[selected_column].str[4:6] == ") ") & (
dataset[selected_column].str[9] == "-") & (
~dataset[selected_column].str.contains("/", na=False))]
d714 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("(0", na=False)) & (
dataset[selected_column].str[5:7] == ") ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d814 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0(", na=False)) & (
dataset[selected_column].str[5:7] == ") ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d914 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("90(", na=False)) & (
dataset[selected_column].str[6] == ")") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d1014 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ") & (
dataset[selected_column].str.endswith(" /", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1114 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0 (", na=False)) & (
dataset[selected_column].str[6] == ")") & (
~dataset[selected_column].str.contains("-", na=False)) & (
~dataset[selected_column].str.contains("/", na=False))]
d1214 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("(", na=False)) & (
dataset[selected_column].str[4] == ")") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11] == " ") & (
~dataset[selected_column].str.contains("-", na=False)) & (
~dataset[selected_column].str.contains("/", na=False))]
d1314 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == "-") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11] == " ") & (
~dataset[selected_column].str.contains("/", na=False))]
d1414 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == "-") & (
dataset[selected_column].str[8] == "-") & (
dataset[selected_column].str[11] == "-") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d1514 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == ")") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1614 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("(", na=False)) & (
dataset[selected_column].str[4:6] == ") ") & (
dataset[selected_column].str[9] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1714 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0 ", na=False)) & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1814 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ") & (
dataset[selected_column].str[6] == " ") & (
dataset[selected_column].str[9] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1914 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[10] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d2014 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 14) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ") & (
dataset[selected_column].str[8] == "-") & (
dataset[selected_column].str[11] == "-") & (
~dataset[selected_column].str.contains("/", na=False))]
d115 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[1] == " ") & (
dataset[selected_column].str[5] == " ") & (
dataset[selected_column].str[10] == " ") & (
dataset[selected_column].str[13] == " ")]
d215 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0 ", na=False)) & (
dataset[selected_column].str[5] == " ") & (
dataset[selected_column].str[9] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d315 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str[3:5] == " ") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11:13] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d415 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11:13] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d515 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0(", na=False)) & (
dataset[selected_column].str[5:7] == ") ") & (
dataset[selected_column].str[10] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d615 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str[3:6] == " / ") & (
dataset[selected_column].str[9] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("-", na=False))]
d715 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0 ", na=False)) & (
dataset[selected_column].str[5] == " ") & (
dataset[selected_column].str[8] == " ") & (
dataset[selected_column].str[11] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d815 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4:6] == "- ") & (
dataset[selected_column].str[9] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("/", na=False))]
d915 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4:6] == " ") & (
dataset[selected_column].str[9] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1015 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0 (", na=False)) & (
dataset[selected_column].str[6:8] == ") ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1115 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("(0", na=False)) & (
dataset[selected_column].str[5:7] == ") ") & (
dataset[selected_column].str[10] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1215 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0-", na=False)) & (
dataset[selected_column].str[5] == "-") & (
dataset[selected_column].str[9] == "-") & (
dataset[selected_column].str[12] == "-") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains(" ", na=False))]
d1315 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("(", na=False)) & (
dataset[selected_column].str[4:6] == ") ") & (
dataset[selected_column].str[9] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1415 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("90 ", na=False)) & (
dataset[selected_column].str[6] == " ") & (
dataset[selected_column].str[10] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1515 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4:6] == ") ") & (
dataset[selected_column].str[9] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1615 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0-", na=False)) & (
dataset[selected_column].str[5] == " ") & (
dataset[selected_column].str[9] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("/", na=False))]
d1715 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0", na=False)) & (
dataset[selected_column].str[4] == " ") & (
dataset[selected_column].str[8:10] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("/", na=False)) & (
~dataset[selected_column].str.contains("-", na=False))]
d1815 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 15) & (
dataset[selected_column].str.startswith("0-", na=False)) & (
dataset[selected_column].str[5] == "-") & (
dataset[selected_column].str[9] == " ") & (
dataset[selected_column].str[12] == " ") & (
~dataset[selected_column].str.contains("/", na=False))]
d116 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 16) & (
dataset[selected_column].str.startswith("(0", na=False)) & (
dataset[selected_column].str[5:7] == ") ") & (
dataset[selected_column].str[10] == " ") & (
dataset[selected_column].str[13] == " ")]
d216 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 16) & (
dataset[selected_column].str.startswith("+90 ", na=False)) & (
dataset[selected_column].str[7] == " ") & (
dataset[selected_column].str[11] == " ")]
d316 = dataset[selected_column][(dataset[selected_column].astype(str).map(len) == 16) & (
dataset[selected_column].str.startswith("0(", na=False)) & (
dataset[selected_column].str[5:7] == ") ") & (
dataset[selected_column].str[10] == " ") & (