mask = (df['date'] >= start_date) & (df['date'] <= end_date)
df2 = df.loc[mask]
df2 = df[df['column_name']== 'filter_value']
df = df.drop(['column_name'], axis=1)
cols = df.columns.tolist()
cols = cols[-1:] + cols[:-1]
df = df[cols]
df = df.rename(columns={'old_name': 'new_name'})
df = df.merge(df2,on='shared_column_name')
df = df.groupby(["column_a", "column_b"]).sum().reset_index()
df.sort_values(by=['col1'],ascending=False)
new_list = df['column'].tolist()
string_name = ','.join([str(elem) for elem in list_name])
df['column'] = df['column'].str.strip()
df['column'] = df['column'].str.lower()
print(len(df.index))
df.to_csv('file.csv')
df = pd.read_csv('file.csv')
df['month'] = df['date'].values.astype('datetime64[M]')
df['date'] = pd.to_datetime(df['date'], infer_datetime_format=True).dt.date
date = date.date()
start_date = datetime.strptime(start_date, '%Y-%m-%d')
end_date = datetime.strptime(end_date, '%Y-%m-%d')
days = (end_date - start_date).days
df = df.groupby(pd.Grouper(key="date", freq="1M")).sum().reset_index()
df['column'] = df['column'].str.replace('$', '')
df['column'] = df['column'].astype('datetime64[ns]')
df['column'] = df['column'].fillna(0)
df.loc[(df["column"].isin(["a", "b", "c", "d"]), "column")] = "new_value"
df['column'] = df['column'].replace({'\$ -': '','\$': '', ',': ''}, regex=True).astype(float)
df['column_b'] = df['column_a'].apply(lambda x: 1 if x > 0 else 0)
def div(x, y):
if y > 0:
return round(x / y, 2)
else:
return 1
df['percentage'] = df.apply(lambda x: div(x['column_a'], x['column_b']), axis=1)