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I might want to know how to use For circle to consequently gather important information since I can do physically individually.

Here is the example information (https://ideone.com/eypcRj).

This information is Google's people group portability information in the Netherlands which is openly accessible.

GM_combined = pd.read_csv("textfile.txt") #the URL text document here

#A touch of cleaning GM_combined = GM_combined.rename(columns={'date': 'Date'})

GM_combined["Date"] = pd.to_datetime(GM_combined["Date"]) GM_combined.sort_values(by='Date', ascending=True) GM_region_1 = GM_combined.loc[(GM_combined['place_id'] != 'ChIJu-SH28MJxkcRnwq9_851obM')] GM_region_1['sub_region_1'].unique()

What I need to do is to code circling which gets:

a new dataframe for each "sub_region_1" with a dataframe name setted as the name of every area - - - allude (1) each dataframe of the sub_region_1 has mean worth of segments beneath gathered by "Date" - - - allude (2) 'retail_and_recreation_percent_change_from_baseline', 'grocery_and_pharmacy_percent_change_from_baseline', 'parks_percent_change_from_baseline', 'transit_stations_percent_change_from_baseline', 'workplaces_percent_change_from_baseline', 'residential_percent_change_from_baseline' rename these sections in 2 as "Sub-district 1-name_following": - - - allude (3) 'retail_and_recreation_percent_change_from_baseline': 'Sub-district 1-name_Retail_Rec_%Change', 'grocery_and_pharmacy_percent_change_from_baseline': 'Sub-district 1-name_Grocery_Pharma_%Change',

'parks_percent_change_from_baseline': 'Sub-district 1-name_Parks_%Change',

'transit_stations_percent_change_from_baseline': 'Sub-district 1-name_Transit_%Change',

'workplaces_percent_change_from_baseline': 'Sub-district 1-name_Workplace_%Change',

'residential_percent_change_from_baseline': 'Sub-district 1-name_Resident_%Change'

4. change the date section in each of new dataframe of sub_region_1 - - - allude (4)

#the name of dataframe can rely upon each of new dataframe Drenthe["Date"] = pd.to_datetime(Drenthe["Date"]) Drenthe.set_index('Date', inplace=True) Drenthe = Drenthe.asfreq("D")

Essentially, I have this manual coding underneath for every area (I just put one district as an example), however in the event that you can assist me with utilizing For Loop to naturally create a new dataframe per sub_region_1, I would be truly apprecaited.

Drenthe = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Drenthe')] #(1)

###I mannually coded new dataframe per locale like this. #Flevoland = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Flevoland')] #Friesland = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Friesland')] #Gelderland = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Gelderland')] #Groningen = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Groningen')] #Limburg = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Limburg')] #North_Brabant = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'North Brabant')] #North_Holland = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'North Holland')] #Overijssel = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Overijssel')] #South_Holland = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'South_Holland')] #Utrecht = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Utrecht')] #Zeeland = GM_region_1.loc[(GM_region_1['sub_region_1'] == 'Zeeland')]

#I might want to emphasize this cycle per "sub_region_1" area. #(2) Drenthe = Drenthe.groupby('Date', as_index=False, sort=False)['retail_and_recreation_percent_change_from_baseline', 'grocery_and_pharmacy_percent_change_from_baseline', 'parks_percent_change_from_baseline', 'transit_stations_percent_change_from_baseline', 'workplaces_percent_change_from_baseline', 'residential_percent_change_from_baseline'].mean()

#(3) Drenthe = Drenthe.rename(columns={'retail_and_recreation_percent_change_from_baseline': 'Drenthe_Retail_Rec_%Change', 'grocery_and_pharmacy_percent_change_from_baseline': 'Drenthe_Grocery_Pharma_%Change', 'parks_percent_change_from_baseline': 'Drenthe_Parks_%Change', 'transit_stations_percent_change_from_baseline': 'Drenthe_Transit_%Change', 'workplaces_percent_change_from_baseline': 'Drenthe_Workplace_%Change', 'residential_percent_change_from_baseline': 'Drenthe_Resident_%Change'})

#(4) Drenthe["Date"] = pd.to_datetime(Drenthe["Date"]) Drenthe.set_index('Date', inplace=True) Drenthe = Drenthe.asfreq("D")

PS: as I just included 400 line of test information, you may not track down each district in this code.

I didn't check the number of interesting districts are remembered for this example text document.

As I don't know such a huge amount about circling, I would truly see the value in it in the event that you can give point by point request of clarifications to how you did as such I can apply in the future as well.p

Bizarrely high grouping of metals in drinking water can represent a wellbeing peril. Twenty couples of information were taken from various areas of Ku? Cenneti Lake estimating zinc focus in base water and surface water. Table 1.Zinc Concentration (mg/l) in base and surface water in various locales and areas of Karacakaan Lake Location Region Area Zinc_Bottom Zinc Surface Zinc_ Mean TZ3_Mean Area 0.51 2.67 0.49 0.50 Loc 1 North An Area 0.46 2.45 0.45 0.46 North 0.46 A Loc 2 Area 0.52 2.66 0.52 0.52 Loc 3 North An Area 0.50 0.50 2.56 0.51 Loc 4 North An Area 0.58 3.03 0.57 0.58 Loc 5 North A

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table ORDER? [Chapter 7 Telecommunications] 2. Figure 7-1 depicts a simple computer network. Describe what each component works to make a communication with a remote entity in the internet. (5 points) PC Switch Server NIC- NOS Switch NIC PC Figure 7-1 A computer network Router Other Networks Internet a. Why do we need Network Interface Card (NIC) Card to communicate with other communications entities?