# duplicate_num = df3.duplicated().sum()
# duplicate_num
df3.drop_duplicates()
finding out the duplicated values and their sum and dropping out them

df3.InvoiceNo.str.contains('C').sum() specially categorizing by column and using .str.contains('C') which stands for finding out the C letter in that column

df3 = df3[df3['Quantity'] > 0]
filtering out where Quantity is more than

by_country = df3 \
.query("Country == 'Germany'") \
.aggregate({'CustomerID': 'count'})

taking firstly database then .querying('column == "Country"')
then aggregating

percentile_80 = df3['InvoiceNo'].value_counts().quantile(0.8)
filtered_users = df3['InvoiceNo'].value_counts()[df3['InvoiceNo'].value_counts() > percentile_80]
germany_top = filtered_users.index.unique()
germany_top


using filtering function with percentile

top_retail_germany = df3[df3['InvoiceNo'].isin(germany_top)]
top_retail_germany



finding out the column which contains in another column also

df3_g = df3 \
.groupby(['StockCode', 'Description'], as_index=False) \
.aggregate({'Quantity': 'count'}) \
.sort_values('Quantity', ascending=False)
df3_g


grouping by sorting and agg

df3.loc[:, 'Revenue'] = df3['Quantity'] * df3['UnitPrice']
df3

using loc with: only which stands for all then with comma, using column name for assigning it

df3_total_Revenue = df3 \
.groupby('InvoiceNo')['Revenue'].sum()

using groupby by Invoice No which means individually and by Revenue which totally means Individual Income

pd.to_datetime(ads_data.time, unit='s') with this you will make seconds carefully look

pd.to_datetime(ads_data.date) date only

ads_data.groupby('date') \
.agg({'ad_id': 'count'}).plot()

using groupby and aggregating and ploting

ads_data.groupby(['date', 'event'], as_index=False) \
.agg({'ad_id': 'count'}) \
.pivot(index='date', columns='event', values='ad_id')\
.reset_index()


using groupby and pivoting by date and event and ad_id

ads_data[ads_data.date == '2019-04-05'] \
.groupby('ad_id') \
.agg({'time': 'count'}) \
.sort_values('time', ascending=False) \
.head()

specifiying time and aggregating and sorting out the value time

ads_data.query('ad_id == @ad_id').head(1) with query you will find out the this one

.pivot(index='ad_id', columns='event', values='time').reset_index() this with reset_index helps to start index by again

ads_data_by_ad.assign(ctr = ads_data_by_ad.click / ads_data_by_ad.view,
ctr_per = 100 * ads_data_by_ad.click / ads_data_by_ad.view)

when there is 2 variables and you are assinging that in one table new variables aare in here

ads_data_by_ad.query('view == 0').ad_id finding out the missing column

ads_data[ads_data.ad_id.isin(ads_ids_bug)] finding out the wrong things

ads_data[ads_data.ad_id == 16548].sort_values('time') sorting out with time