Linear Regression - Univariate regression - Statsmodels

def ffill_cols(df, cols_to_fill_name='Unn'):
    """
    Forward fills column names. Propagate last valid column name forward to next invalid column. Works similarly to pandas
    ffill().
    
    :param df: pandas Dataframe; Dataframe
    :param cols_to_fill_name: str; The name of the columns you would like forward filled. Default is 'Unn' as
    the default name pandas gives unnamed columns is 'Unnamed'
    
    :returns: list; List of new column names
    """
    cols = df.columns.to_list()
    for i, j in enumerate(cols):
        if j.startswith(cols_to_fill_name):
            cols[i] = cols[i-1]
    return cols

df['SettlementDate'] = pd.TimedeltaIndex(df['SettlementDate'], unit='d') + dt.datetime(1900,1,1)

const refunds = [
  {
    request_amount_to_refund: "3000",
    entity_id: "1850231228424",
    is_zni: false
  },
  {
    request_amount_to_refund: "2000",
    entity_id: "1850231228422",
    is_zni: false
  },
  {
    request_amount_to_refund: "1000",
    entity_id: "1850231228422",
    is_zni: true
  },
  {
    request_amount_to_refund: "500",
    entity_id: "1850231228422",
    is_zni: true
  }
]
    
const refundsFormatted = Object.values(refunds.reduce((acc, curr) => {
  const valueId = `${curr.entity_id}-${curr.is_zni}`

  // Does value already exist ?
  const existingValue = acc[valueId]

  // Values to add
  const existingRequestAmount = parseInt(existingValue?.request_amount_to_refund ?? 0)
  const newRequestAmount = parseInt(curr?.request_amount_to_refund ?? 0)
  const quantity = existingValue?.quantity ? existingValue?.quantity + 1 : 1

  const newValue = {
    ...curr,
    request_amount_to_refund: existingRequestAmount + newRequestAmount,
    quantity: quantity
  }
  return ({
    ...acc,
    [valueId]: newValue,
  })
}, {}))

console.log(refundsFormatted)

/**
Output expected 

[
  {
    request_amount_to_refund: "3000",
    entity_id: "1850231228424",
    is_zni: false,
    quantity: 1
  },
  {
    request_amount_to_refund: "2000",
    entity_id: "1850231228422",
    is_zni: false,
    quantity: 1
  },
  {
    request_amount_to_refund: "1500",
    entity_id: "1850231228422",
    is_zni: true,
    quantity: 2
  }
]
*/

sales.groupby('Platform').get_group('X360')

platform_names = {
    'PS3':'Playstation',
    'PS4':'Playstation',
    'X360':'Xbox',
    'XOne':'Xbox'
}

sales.set_index('Platform').groupby(platform_names).sum()

df.groupby('name')['n'].sum().plot(kind='bar', x='name')

## MAP (for Series)
series.map(function) 
Series.map({mapping dict})

#add a column to your df operating on each row
# Let's call this "custom_sum" as "sum" is a built-in function
def custom_sum(row):
    return row.sum()
df['D'] = df.apply(custom_sum, axis=1)

#add as row
df.loc['Row 5'] = df.apply(custom_sum, axis=0)

## APPLY (for DataFrame)
df.apply(lambda col: col.max(), axis = 0)     # default axis
df.apply(lambda row: row[‘A’] + row[‘B’], axis = 1)
df.applymap(my_funct_for_indiv_elements)
    df.applymap(lambda x: '%.2f' % x)

## GROUPBY
group = df.groupby('col_A')
group.mean()
group.apply(np.mean)
group.agg({
    col_A: ['mean', np.sum],
    col_B: my_custom_sum,
    col_B: lambda s: my_custom_sum(s)
    })

group.apply(custom_mean_function)

import statsmodels.formula.api as smf

model1 = smf.ols('review_score ~ wait_time', data=orders).fit()
print(model1.summary())

df['column_name'] = pd.to_datetime(df['column_name'])
# new version
df.groupby(pd.Grouper(key='column_name', freq="M")).mean().plot()

df = df[["C", "A", "B"]]

df.drop_duplicates()

'${:,.2f}'.format(dfCombined['Amount'].sum())

(df.groupby('name')['ext price']
 .agg(['mean', 'sum'])
 .style.format('${0:,.2f}'))

# importing pandas as pd 
import pandas as pd 
  
# Creating the dataframe  
df = pd.read_csv("nba.csv") 
  
# Using regular expression to extract all 
# columns which has letter 'a' or 'A' in its name. 
df.filter(regex ='[aA]') 

# applying filter function 
df.filter(["Name", "College", "Salary"]) 

c3 = pd.Series(['China', 'US'])
df[df['countries'].isin(c1)]