<jupyter_start><jupyter_text>Import dependencies and libraries<jupyter_code>%matplotlib inline
#Dependencies
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
from scipy import stats

pd.set_option("display.float_format", lambda x:"%.2f" % x)<jupyter_output><empty_output><jupyter_text>Import and read CSVs<jupyter_code>#Files to Load for THE YEAR 2020
public_school_path = "./uc_data1.csv"
private_school_path = "./private_school_data.csv"
county_income = "/County income.xlsx"

#Read the data file
public_school = pd.read_csv(public_school_path)
public_school['Institution_type'] = 'Public'
public_school['Year'] = 2020
private_school = pd.read_csv(private_school_path)
private_school['Institution_type'] = 'Private'
private_school['Year'] = 2020
##Files to Load for THE YEAR 2019
public_snine_path = "./public_s_nine.csv"
private_snine_path = "./private_s_nine.csv"

#Read the data file
nine_public = pd.read_csv(public_snine_path)
nine_public['Institution_type'] = 'Public'
nine_public['Year'] = 2019

nine_private = pd.read_csv(private_snine_path)
nine_private['Institution_type'] = 'Public'
nine_private['Year'] = 2019
# Files to Load for THE YEAR 2018
public_seight_path = "./public_s_eight.csv"
private_seight_path = "./private_s_eight.csv"

# Read the data file
eight_public = pd.read_csv(public_seight_path)
eight_public['Institution_type'] = 'Public'
eight_public['Year'] = 2018

eight_private = pd.read_csv(private_seight_path)
eight_private['Institution_type'] = 'Public'
eight_private['Year'] = 2018
#Files to Load for THE YEAR 2017
public_sseven_path = "./public_s_seven.csv"
private_sseven_path = "./private_s_seven.csv"

#Read the data file
seven_public = pd.read_csv(public_sseven_path)
seven_public['Institution_type'] = 'Public'
seven_public['Year'] = 2017

seven_private = pd.read_csv(private_sseven_path)
seven_private['Institution_type'] = 'Public'
seven_private['Year'] = 2017
#Files to Load for THE YEAR 2016
public_ssix_path = "./public_s_six.csv"
private_ssix_path = "./private_s_six.csv"

#Read the data file
six_public = pd.read_csv(public_ssix_path)
six_public['Institution_type'] = 'Public'
six_public['Year'] = 2016

six_private = pd.read_csv(private_ssix_path)
six_private['Institution_type'] = 'Public'
six_private['Year'] = 2016<jupyter_output><empty_output><jupyter_text>Preview Data<jupyter_code># Display the data table for preview
uc_data_complete_2020 = pd.concat([public_school, private_school])
uc_data_complete_2020
# Display the data table for preview
nine_data_complete = pd.concat([nine_public, nine_private])
nine_data_complete
# Display the data table for preview
eight_data_complete = pd.concat([eight_public, eight_private])
eight_data_complete
# Merge & Display the data table for preview
seven_data_complete = pd.concat([seven_public, seven_private])
seven_data_complete
# Merge & Display the data table for preview
six_data_complete = pd.concat([six_public, six_private])
six_data_complete<jupyter_output><empty_output><jupyter_text>Concatenate UC Data CSV's<jupyter_code>#Merging the data of all 5 years of public and private school data using concat

All_complete_data = pd.concat([uc_data_complete_2020, nine_data_complete,
                               eight_data_complete, seven_data_complete, six_data_complete])
#Re-index data frame
All_complete_data = All_complete_data.reset_index(drop=True)
All_complete_data
#Groupby series to iterate through Count row to see if App is within list, then apply filter if true; iloc to keep indices
All_complete_data = All_complete_data.iloc[All_complete_data.groupby(["Calculation1", "Year"])\
                                .filter(lambda x: "App" in x["Count"].to_list()).index]
#Creating the percent of Applicant Pool column, compare Admission & Enrollment rates across schools
All_complete_data["Application Total"] = All_complete_data.groupby(["Year", "Calculation1"])["All"].transform("max")
All_complete_data["Percent of Applicant Pool"] = All_complete_data["All"]/All_complete_data["Application Total"]*100<jupyter_output><ipython-input-15-6fdc968a1a16>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  All_complete_data["Application Total"] = All_complete_data.groupby(["Year", "Calculation1"])["All"].transform("max")
<ipython-input-15-6fdc968a1a16>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  All_complete_data["Percent of Applicant Pool"] = All_complete_data["All"]/All_complete_data["Application Total"]*100<jupyter_text>Join CA High Schools w/ County Median Income (for top/bottom analysis)<jupyter_code>county_income = "County income.xlsx"
plus_income = pd.read_excel(county_income)
plus_income
#Check county list within County Median Income data frame
plus_income["County"].unique()
#Check county list within UC complete data frame
sorted(All_complete_data["County/State/ Territory"].unique())
#Merge complete UC data and County Income
all_complete_new = All_complete_data.merge(plus_income, how = "left", left_on = "County/State/ Territory", right_on = "County")

#Calculate average median income of CA counties
avg_med_income = all_complete_new["Value"].mean()

all_complete_new["Value Minus Average Median Income"] = all_complete_new["Value"] - avg_med_income
all_complete_new<jupyter_output><empty_output><jupyter_text>Look at % of Public/Private with Admission over 80% (compared to applied)<jupyter_code>#Count of 80%+ acceptance of public school applicants (into the UC System)
public_adm_count = all_complete_new[(all_complete_new["Count"] == "Adm")
                  & (all_complete_new["Percent of Applicant Pool"] >= 80)
                  & (all_complete_new["Institution_type"] == "Public")]["Calculation1"].count()

total_public_count = all_complete_new[(all_complete_new["Count"] == "App") &
                  (all_complete_new["Institution_type"] == "Public")]["Calculation1"].count()

public_adm_count/total_public_count*100
print("Public: %.2f" % (public_adm_count/total_public_count*100) + "%")
#Count of 80%+ acceptance of private school applicants (into the UC System)
private_adm_count = all_complete_new[(all_complete_new["Count"] != "App")
                                      & (all_complete_new["Percent of Applicant Pool"] >= 80)
                                      & (all_complete_new["Institution_type"] == "Private")]["Calculation1"].count()

total_private_count = all_complete_new[(all_complete_new["Count"] == "App") &
                  (all_complete_new["Institution_type"] == "Private")]["Calculation1"].count()

private_adm_count/total_private_count*100
print("Private: %.2f" % (private_adm_count/total_private_count*100) + "%")<jupyter_output>Private: 22.73%<jupyter_text>Sum All UC Data (2016 - 2020)<jupyter_code>#Filter the whole data set to see total of admissions
all_complete_new[all_complete_new["Count"]=="Adm"]["All"].sum()
#Filter the whole data set to see total of applications for 2016-2020
all_complete_new[all_complete_new["Count"]=="App"]["All"].sum()
#Filter the whole data set to see total of enrollment for 2016-2020
all_complete_new[all_complete_new["Count"]=="Enr"]["All"].sum()<jupyter_output><empty_output><jupyter_text>Overall CA Public & Private Applicants - Bar Chart<jupyter_code>#Bar Graph for OVERALL both private and public UC Application Statistics 2016-2020
x_value = ["Application","Admission", "Enrolled"]
y_value = [566344, 364811, 186914]
plt.title("UC Applicant Statistics For Both Public and Private Schools for 2016-2020")
plt.xlabel("UC Application Status")
plt.ylabel("Number of California High School Students")
plt.bar(x_value,y_value,color=['orange', 'lightcoral', 'green']);<jupyter_output><empty_output><jupyter_text>Double-Filter Applications for Public/Private by Year & Sum<jupyter_code>#Double Filter for the number of applications for both public & private for the year 2016
all_complete_new[(all_complete_new["Year"]== 2016) & (all_complete_new["Count"] == "App")]["All"].sum()
#Double Filter for the number of applications for both public & private for the year 2017
all_complete_new[(all_complete_new["Year"]== 2017) & (all_complete_new["Count"] == "App")]["All"].sum()
#Double Filter for the number of applications for both public & private for the year 2018
all_complete_new[(all_complete_new["Year"]== 2018) & (all_complete_new["Count"] == "App")]["All"].sum()
#Double Filter for the number of applications for both public & private for the year 2019
all_complete_new[(all_complete_new["Year"]== 2019) & (all_complete_new["Count"] == "App")]["All"].sum()
#Double Filter for the number of applications for both public & private for the year 2020
all_complete_new[(all_complete_new["Year"]== 2020) & (all_complete_new["Count"] == "App")]["All"].sum()<jupyter_output><empty_output><jupyter_text>Line Graph for Application Count Trend (2016 - 2020)<jupyter_code>#Using a Line Graph to set my values to see the Trend for Overall UC Application Statistics 2016-2020 
x_value = [2016, 2017, 2018, 2019, 2020]
y_value = [105749, 112340, 119828, 115675, 112752]
plt.title("UC Application Statistics for each 2016-2020")
plt.xlabel("Year")
plt.ylabel("Number of California High School Students That Applied")
plt.plot(x_value,y_value)
plt.xticks(x_value)
plt.show()<jupyter_output><empty_output><jupyter_text>Double-Filter Admissions for Public/Private by Year & Sum<jupyter_code>#Double Filter for the number of admissions for both public & private for the year 2016
all_complete_new[(all_complete_new["Year"]== 2016) & (all_complete_new["Count"] == "Adm")]["All"].sum()
#Double Filter for the number of admissions for both public & private for the year 2017
all_complete_new[(all_complete_new["Year"]== 2017) & (all_complete_new["Count"] == "Adm")]["All"].sum()
#Double Filter for the number of admissions for both public & private for the year 2018
all_complete_new[(all_complete_new["Year"]== 2018) & (all_complete_new["Count"] == "Adm")]["All"].sum()
#Double Filter for the number of admissions for both public & private for the year 2019
all_complete_new[(all_complete_new["Year"]== 2019) & (all_complete_new["Count"] == "Adm")]["All"].sum()
#Double Filter for the number of admissions for both public & private for the year 2020
all_complete_new[(all_complete_new["Year"]== 2020) & (all_complete_new["Count"] == "Adm")]["All"].sum()<jupyter_output><empty_output><jupyter_text>Line Graph for Addmission Count Trend (2016 - 2020)<jupyter_code>#Using a Line Graph to set my values to see the Trend for Overall UC Admission Statistics 2016-2020
x_value = [2016, 2017, 2018, 2019, 2020]
y_value = [71499, 70078, 71422, 71932, 79880]
plt.title("UC Admission Statistics for each 2016-2020")
plt.xlabel("Year")
plt.ylabel("Number of California High School Students Admitted")
plt.plot(x_value,y_value)
plt.xticks(x_value)
plt.show()<jupyter_output><empty_output><jupyter_text>Double-Filter Enrollment for Public/Private by Year & Sum<jupyter_code>#Double Filter for the number of students enrolled for both public & private for the year 2016
all_complete_new[(all_complete_new["Year"]== 2016) & (all_complete_new["Count"] == "Enr")]["All"].sum()
#Double Filter for the number of admissions for both public & private for the year 2017
all_complete_new[(all_complete_new["Year"]== 2017) & (all_complete_new["Count"] == "Enr")]["All"].sum()
#Double Filter for the number of admissions for both public & private for the year 2018
all_complete_new[(all_complete_new["Year"]== 2018) & (all_complete_new["Count"] == "Enr")]["All"].sum()
#Double Filter for the number of admissions for both public & private for the year 2019
all_complete_new[(all_complete_new["Year"]== 2019) & (all_complete_new["Count"] == "Enr")]["All"].sum()
#Double Filter for the number of admissions for both public & private for the year 2020
all_complete_new[(all_complete_new["Year"]== 2020) & (all_complete_new["Count"] == "Enr")]["All"].sum()<jupyter_output><empty_output><jupyter_text>Line Graph for Enrollment Count Trend (2016 - 2020)<jupyter_code>#Using a Line Graph to set my values to see the trend for Overall UC Enrollment Statistics 2016-2020
x_value = [2016, 2017, 2018, 2019, 2020]
y_value = [38586,36670,37057,36506,38094]
plt.title("UC Enrollment Statistics for each 2016-2020")
plt.xlabel("Year")
plt.ylabel("Number of California High School Students Enrolled")
plt.plot(x_value,y_value)
plt.xticks(x_value)
plt.show()<jupyter_output><empty_output><jupyter_text>Double-Filter Enrollment for Public/Private Separately & Sum (2016 - 2020)<jupyter_code>#Double filter of Public High Schools Number of Applications
all_complete_new[(all_complete_new["Institution_type"]=="Public") & (all_complete_new["Count"] == "App")]["All"].sum()
#Double filter of Private High Schools Number of Applications

all_complete_new[(all_complete_new["Institution_type"]=="Private") & (all_complete_new["Count"] == "App")]["All"].sum()
#Double filter of Public High Schools Number of Admissions

all_complete_new[(all_complete_new["Institution_type"]=="Public") & (all_complete_new["Count"] == "Adm")]["All"].sum()
#Double filter of Private High Schools Number of Admissions

all_complete_new[(all_complete_new["Institution_type"]=="Private") & (all_complete_new["Count"] == "Adm")]["All"].sum()
#Double filter of Public High Schools Number of Enrollment

all_complete_new[(all_complete_new["Institution_type"]=="Public") & (all_complete_new["Count"] == "Enr")]["All"].sum()
#Double filter of Private High Schools Number of Enrollment

all_complete_new[(all_complete_new["Institution_type"]=="Private") & (all_complete_new["Count"] == "Enr")]["All"].sum()<jupyter_output><empty_output><jupyter_text>CA Public High Schools Application Statistics (2016 - 2020)<jupyter_code>#Public graph
x_value = ["Application","Admission", "Enrolled"]
y_value = [552931,355682, 183419 ]
plt.title("UC Application Statistics for Public Schools 2016-2020")
plt.xlabel("UC Application Status")
plt.ylabel("Number of California Public High School Students")
plt.bar(x_value,y_value,color=['orange', 'lightcoral', 'green'])
plt.show();<jupyter_output><empty_output><jupyter_text>CA Private High Schools Application Statistics (2016 - 2020)<jupyter_code>#Private graph
x_value = ["Application","Admission", "Enrolled"]
y_value = [13413,9129, 3494]
plt.title("UC Application Statistics for Private Schools 2016-2020")
plt.xlabel("UC Application Status")
plt.ylabel("Number of California Private High School Students")
plt.bar(x_value,y_value,color=['orange', 'lightcoral', 'green'])
plt.show();<jupyter_output><empty_output><jupyter_text>Display Complete Data Frame<jupyter_code>all_complete_new
#Define data frame for public and private
df_public = all_complete_new[all_complete_new["Institution_type"] == "Public"]
df_private = all_complete_new[all_complete_new["Institution_type"] == "Private"]<jupyter_output><empty_output><jupyter_text>Pull Top 10 Applied, Admitted, Enrolled, CA Public/Private High Schools<jupyter_code>#Top 10 public schools with highest application (count), mean over 5 years where applicable
df1 = df_public[df_public["Count"] == "App"].groupby(["Calculation1","County/State/ Territory"])\
    [["All", "Value", "Value Minus Average Median Income"]].mean().nlargest(10, "All")
df1
#Count of schools below CA median Income
df1[df1["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df1[df1["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Top 10 private schools with highest application (count)
df2 = df_private[df_private["Count"] == "App"].groupby(["Calculation1", "County/State/ Territory"])\
    [["All", "Value", "Value Minus Average Median Income"]].mean().nlargest(10, "All")
df2
#Count of schools below CA median Income
df2[df2["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df2[df2["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Top 10 public schools with highest admissions (count)
df3 = df_public[df_public["Count"] == "Adm"].groupby(["Calculation1", "County/State/ Territory"])\
    [["All", "Value", "Value Minus Average Median Income"]].mean().nlargest(10, "All")
df3
#Count of schools below CA median Income
df3[df3["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df3[df3["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Top 10 private schools with highest admissions (count)
df4 = df_private[df_private["Count"] == "Adm"].groupby(["Calculation1", "County/State/ Territory"])\
    [["All", "Value", "Value Minus Average Median Income"]].mean().nlargest(10, "All")
df4
#Count of schools below CA median Income
df4[df4["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df4[df4["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Top 10 public schools highest enrollment (count)
df5 = df_public[df_public["Count"] == "Enr"].groupby(["Calculation1", "County/State/ Territory"])\
    [["All", "Value", "Value Minus Average Median Income"]].mean().nlargest(10, "All")
df5
#Count of schools below CA median Income
df5[df5["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df5[df5["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Top 10 private schools highest enrollment (count)
df6 = df_private[df_private["Count"] == "Enr"].groupby(["Calculation1", "County/State/ Territory"])\
    [["All", "Value", "Value Minus Average Median Income"]].mean().nlargest(10, "All")
df6
#Count of schools below CA median Income
df6[df6["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df6[df6["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())<jupyter_output>Count of Schools Below CA Median Income: 1<jupyter_text>Pull Bottom 10 Applied, Admitted, Enrolled, CA Public/Private High Schools<jupyter_code>#Bottom 10 public schools with lowest application (count)
df7 = df_public[df_public["Count"] == "App"].groupby(["Calculation1", "County/State/ Territory"])\
    [["All", "Value", "Value Minus Average Median Income"]].mean().nsmallest(10, "All")
df7
#Count of schools below CA median Income
df7[df7["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df7[df7["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Bottom 10 private schools with lowest application (count)
df8 = df_private[df_private["Count"] == "App"].groupby(["Calculation1", "County/State/ Territory"])\
    [["All", "Value", "Value Minus Average Median Income"]].mean().nsmallest(10, "All")
df8
#Count of schools below CA median Income
df8[df8["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df8[df8["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Bottom 10 public schools with lowest admissions (count)
df9 = df_public[df_public["Count"] == "Adm"].groupby(["Calculation1", "County/State/ Territory"])\
[["All", "Value", "Value Minus Average Median Income"]].mean().nsmallest(10, "All")
df9
#Count of schools below CA median Income
df9[df9["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df9[df9["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Bottom 10 private schools with lowest admissions (count)
df10 = df_private[df_private["Count"] == "Adm"].groupby(["Calculation1", "County/State/ Territory"])\
[["All", "Value", "Value Minus Average Median Income"]].mean().nsmallest(10, "All")
df10
#Count of schools below CA median Income
df10[df10["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df10[df10["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Bottom 10 public schools lowest enrollment (count)
df11 = df_public[df_public["Count"] == "Enr"].groupby(["Calculation1", "County/State/ Territory"])\
[["All", "Value", "Value Minus Average Median Income"]].mean().nsmallest(10, "All")
df11
#Count of schools below CA median Income
df11[df11["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df11[df11["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())
#Bottom 10 private schools lowest enrollment (count)
df12 = df_private[df_private["Count"] == "Enr"].groupby(["Calculation1", "County/State/ Territory"])\
[["All", "Value", "Value Minus Average Median Income"]].mean().nsmallest(10, "All")
df12
#Count of schools below CA median Income
df12[df12["Value Minus Average Median Income"] < 0].count()
print("Count of Schools Below CA Median Income:", df12[df12["Value Minus Average Median Income"] < 0]\
      ["Value Minus Average Median Income"].count())<jupyter_output>Count of Schools Below CA Median Income: 4<jupyter_text>Statistical Analysis<jupyter_code>#Percent of applicant pool admitted/enrolled by CA high school for all years where applicable
#Mean is the average number of admission out of applicant pool
public_adm = df_public[df_public["Count"] == "Adm"].describe()["Percent of Applicant Pool"]
public_enr = df_public[df_public["Count"] == "Enr"].describe()["Percent of Applicant Pool"]
private_adm = df_private[df_private["Count"] == "Adm"].describe()["Percent of Applicant Pool"]
private_enr = df_private[df_private["Count"] == "Enr"].describe()["Percent of Applicant Pool"]
all_desc = pd.DataFrame()
all_desc["Public Admission"] = public_adm
all_desc["Public Enrollment"] = public_enr
all_desc["Private Admission"] = private_adm
all_desc["Private Enrollment"] = private_enr
all_desc
df_private.describe()["Percent of Applicant Pool"]
df_public.groupby("Year").describe()["Percent of Applicant Pool"]
print(seven_private[seven_private["Count"] == "App"]["Count"].count(), seven_private[seven_private["Count"] == "Adm"]["Count"].count())<jupyter_output>322 324<jupyter_text>Attempted Code & Notes<jupyter_code># DO NOT NEED FOR OUR PROJECT
# year=uc_data_complete_2020["Year"]
# y = uc_data_complete_2020["Count"]
# plt.scatter(year,y)
# plt.xticks(year, rotation=90)
# plt.xlabel('Year')
# plt.ylabel('Rate')
# plt.show()

# DO NOT NEED FOR OUR PROJECT
## See our full data for 2020 public school
#public_school.head()

# DO NOT NEED FOR OUR PROJECT
## See our full data for 2020 private school
#private_school.head()

# DO NOT NEED IT FOR OUR PROJECT
#Finding the total number of admissions, applications, and enrollment of 2016-2020 (as a whole)

# All_complete_data["Count"].value_counts()

# DO NOT NEED IT FOR OUR PROJECT

# Finding the number of Adm, App, Enr for EACH year (make it into series, like a list)


# All_complete_data.groupby("Year")["Count"].value_counts()

# DO NOT NEED IT FOR OUR PROJECT
#Convert into a dataframe (turn a series into a table, like a matrix to see the rows and columns)
# df = pd.DataFrame (All_complete_data.groupby("Year")["Count"].value_counts())
# df
# df.rename_axis(index={"Count":"Type"}, inplace = True)

# DO NOT NEED IT FOR OUR PROJECT
#df

# DO NOT NEED IT FOR OUR PROJECT
#df.reset_index(inplace=True)

# DO NOT NEED IT FOR OUR PROJECT

#df

# DO NOT NEED IT FOR OUR PROJECT

#### Side note: we should make the bars a bit more cleaner maybe in percentages?
#pivot data to be able to generate each column of Adm, App, Enr in a bar graph


#df.pivot("Year","Type", "Count").plot(kind="bar")

# DO NOT NEED IT FOR OUR PROJECT
### We need to fix the settings , bascially the overall data visualization for it to be more readable.
# Generate a scatter plot of year versus Adm,App,Enr
# year=df["Year"]
# y = df["Count"]
# plt.scatter(year,y)
# plt.xticks(year, rotation=90)
# plt.xlabel('Year')
# plt.ylabel('Count')
# plt.show()

#Top 10 schools with highest application (count and percentage)
#All_complete_data or separate it by year (that will seem easier?) ---> ex.uc_data_complete_2020
#count()
#value_counts()
#list(uc_datacomplete_2020)
#.head()
#print("Highest application is:", max(list))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1341851747349752, 'fraction_numerical': 0.03313313716796324, 'mean_word_length': 6.638546798029557, 'pattern_counts': {'":': 1, '<': 110, '<?xml version=': 0, '>': 89, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '22', 'dir': 'jupyter-structured-clean-dedup', 'id': '72278', 'license': 'MIT', 'n_tokens_mistral': 8632, 'n_tokens_neox': 7689, 'n_words': 2227, 'path': 'Project 1 - Final.ipynb', 'repo_name': 'duhmonque/Project-1', 'size': '24809'}