Iran History

Iran History

import pandas as pd
import numpy as np

#Generating Years we want to study
Persian_years = list(range(1161,1397))
y = str(len(Persian_years))
print(y + ' years history of Iran')
type(Persian_years[1])

#Generating the kings datastructures 
Qhajar_kings_Gregorian = [['Agha Mohammad Khan' , pd.Timestamp(1789), pd.Timestamp(1797)] , 'Fath-Ali Shah (1797–1834)', 'Mohammad Shah (1834–1848)',
                'Naser al-Din Shah (1848–1896)', 'Mozaffar al-Din Shah (1896–1907)', 'Mohammad Ali Shah (1907–1909)', 'Ahmad Shah (1909–1925)']


Pahlavi_kings_Gregorian = [['Reza Shah Pahlavi', pd.Timestamp('1925.12.15'), pd.Timestamp('16 September 1941')],
                           ['MohammadReza Shah Pahlavi', pd.Timestamp('16 September 1941'), pd.Timestamp('11 February 1979')]]
Constitutional_kings = Qhajar_kings_Gregorian[4:] + Pahlavi_kings_Gregorian #list manipulation

#Jalali / Persian Dates
Qhajar_kings_Jalali = [['Agha Mohammad Khan', 1161,1175] , ['Fath-Ali Shah',1176,1213], ['Mohammad Shah',1213,1226],
                ['Naser al-Din Shah',1226,1275], ['Mozaffar al-Din Shah', 1275,1285], ['Mohammad Ali Shah',1285,1288],
                ['Ahmad Shah',1288,1304]]

Qhajar_kings_Jalali[0:4] #list slicing: returns the first to the forth item in the list

Pahlavi_kings_Jalali = [['Reza Shah Pahlavi',1304,1320],['MohammadReza Shah Pahlavi',1320,1357]]

IR_leaders_Jalali = [['Khomeini',1357,1368],['Khamenei',1368,1396]]
#numpy array for element-wize calculations 
#numpy assumes that all elements are the same type in contrast to list
np_2Darray = np.array(Pahlavi_kings_Gregorian) #2Dimentional array, array of list of list
date_end_RezaShah = np_2Darray[0][2] # same as np np_2Darray[0,2] : first row third column
np_2Darray[:,1:3] #all rows , second and thrid column

'''Review Data Structures'''
'''Series and DataFrames are data structures from pandas'''
#series can be thought of as one-dimentional array/list or a coulmn in a table but series can have lables
#Series can have elements of different types just like list , but in contrast to numpy arrays
#DataFrames can be defined as 2-dimentional labled data structures with columns of dfferent types
df = pd.DataFrame(Persian_years)
df.rename(columns={0:'Persian Year'},inplace=True)
df['Gregorian Year'] = 'convert' # Jalali to Gregorian
#Creating Dynasty Column
s_dynasty = pd.Series(index = Persian_years)
for i in s_dynasty.index:
    if (i >= 1161) & (i <1304):
        s_dynasty[i] = 'Qhajar'
    elif (i >= 1304) & (i < 1357):
        s_dynasty[i] = 'Pahlavi'
    elif (i >= 1357) & (i <=1396):
        s_dynasty[i] = 'Islamic Republic'
df_dynasty = pd.DataFrame(s_dynasty, columns=['Dynasty'])

df = df.join(df_dynasty, how='inner', on='Persian Year')
#Creating King Column
k_list = Qhajar_kings_Jalali + Pahlavi_kings_Jalali + IR_leaders_Jalali 
df_kings = pd.DataFrame(k_list, columns=['King','Crowned Year', 'Ended'])

s_kings = pd.Series(index = Persian_years)

#filling the Series with the king name indexed by the year
for j in s_kings.index:
    for k in df_kings.index:
        if(j >= df_kings.iloc[k][1]) & (j <= df_kings.iloc[k][2]):
            s_kings[j] = df_kings.iloc[k][0]
        
#Adding the king names Series to the DataFrame

df_k = pd.DataFrame(s_kings, columns=['King'])
df = df.join(df_k, how='inner', on='Persian Year')

#Creating PM (Prime Minister) DataFrame

'''!pip install --upgrade pip
!pip install --upgrade ipython jupyter
!pip install xlrd'''
import xlrd

df_PMs = pd.read_excel(io='Iran-PMs.xlsx')
df_PMs.drop('No.', axis = 1, inplace=True)
#df_PMs.set_index(['P-Year Entered Office','P-Year Left Office'],inplace= True)

#df['Prime Minister'] =  P-Year Entered Office   P-Year Left Office

df_PMs_Summary = df_PMs[[ 'Name', 'P-Year Entered Office','P-Year Left Office']]
#df_PMs_Summary[df_PMs_Summary.index == 1357]['Name']
#df_PMs_Summary.loc[79]['Name'] #this returns 'Mir-Hossein Mousavi' and the type is str

# filling the Series with the PM name indexed by the year
s_PM = pd.Series(index = Persian_years)
string = ''
for i in s_PM.index: 
    for j in df_PMs_Summary.index:
        if (i >= df_PMs.iloc[j]['P-Year Entered Office']) & (i <= df_PMs.iloc[j]['P-Year Left Office']):
            string = string  + df_PMs.iloc[j]['Name'] + ' , '
    s_PM[i] = string
    string = ''
s_PM[120:210] #returns row 120 to 205 *** works for both Series and DataFrames ***


#Adding PM (Prime Minister) Column 

df_p = pd.DataFrame(s_PM, columns=['Prime Ministers']).reset_index()
df_p.columns=['Persian Year', 'Prime Ministers']
df = df.merge(df_p, how='inner', on='Persian Year')

df[120:210]

#Creating Important events DataFrame/List/Series

#Creating influential people DataFrame including gender which can later be queried for influential women