Pandas

pd.read_csv (r"") for reading the file

df.isna().sum() is used to find out the sum value of the isnull values

df['date'] = pd.to_datetime(df.date) changin the column for datetime column

for_bar = df['transaction'].value_counts() used for counting values

for_bar.plot(kind ='bar', color = ['green', 'red'],) for viz of plot with conditions

transc = df.query('transaction == "successfull"') \
.groupby('name', as_index=False) \
.agg({'transaction': 'count'})

databaseitself.query('columnname == "filter to use"') \
.groupby('name', as_index = False is used not taking as a index column) \
.agg({'transaction' : 'count'})

pd.read_csv(r'C:\Users\99899\Downloads\why.zip',encoding='windows-1251', compression = 'zip') using reading when it is zip file

# 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