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PyCitySchools_Challenge_starter_code.py
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PyCitySchools_Challenge_starter_code.py
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#!/usr/bin/env python
# coding: utf-8
# In[9]:
# Dependencies and Setup
import pandas as pd
# File to Load (Remember to change the path if needed.)
school_data_to_load = "Resources/schools_complete.csv"
student_data_to_load = "Resources/students_complete.csv"
# Read the School Data and Student Data and store into a Pandas DataFrame
school_data_df = pd.read_csv(school_data_to_load)
student_data_df = pd.read_csv(student_data_to_load)
# Cleaning Student Names and Replacing Substrings in a Python String
# Add each prefix and suffix to remove to a list.
prefixes_suffixes = ["Dr. ", "Mr. ","Ms. ", "Mrs. ", "Miss ", " MD", " DDS", " DVM", " PhD"]
# Iterate through the words in the "prefixes_suffixes" list and replace them with an empty space, "".
for word in prefixes_suffixes:
student_data_df["student_name"] = student_data_df["student_name"].str.replace(word,"")
# Check names.
student_data_df.head(10)
# ## Deliverable 1: Replace the reading and math scores.
#
# ### Replace the 9th grade reading and math scores at Thomas High School with NaN.
# In[121]:
# Install numpy using conda install numpy or pip install numpy.
# Step 1. Import numpy as np.
import numpy as np
# In[130]:
# Step 2. Use the loc method on the student_data_df to select all the reading scores from the 9th grade at Thomas High School and replace them with NaN.
student_data_df.loc[(student_data_df["school_name"] == "Thomas High School") & (student_data_df["grade"] == "9th")], [:, "reading_score"]]=np.NaN} #,[:, "reading_score"]= np.NaN
#student_data_df.loc[:, "reading_score"]= np.NaN
# In[123]:
# Step 3. Refactor the code in Step 2 to replace the math scores with NaN.
student_data_df.loc[:, "math_score"]= np.NaN
# In[126]:
# Step 4. Check the student data for NaN's.
student_data_df.tail(10)
# ## Deliverable 2 : Repeat the school district analysis
# ### District Summary
# In[6]:
# Combine the data into a single dataset
school_data_complete_df = pd.merge(student_data_df, school_data_df, how="left", on=["school_name", "school_name"])
school_data_complete_df.head()
# In[7]:
# Calculate the Totals (Schools and Students)
school_count = len(school_data_complete_df["school_name"].unique())
student_count = school_data_complete_df["Student ID"].count()
# Calculate the Total Budget
total_budget = school_data_df["budget"].sum()
# In[8]:
# Calculate the Average Scores using the "clean_student_data".
average_reading_score = school_data_complete_df["reading_score"].mean()
average_math_score = school_data_complete_df["math_score"].mean()
# In[9]:
# Step 1. Get the number of students that are in ninth grade at Thomas High School.
# These students have no grades.
# Get the total student count
student_count = school_data_complete_df["Student ID"].count()
# Step 2. Subtract the number of students that are in ninth grade at
# Thomas High School from the total student count to get the new total student count.
# In[10]:
# Calculate the passing rates using the "clean_student_data".
passing_math_count = school_data_complete_df[(school_data_complete_df["math_score"] >= 70)].count()["student_name"]
passing_reading_count = school_data_complete_df[(school_data_complete_df["reading_score"] >= 70)].count()["student_name"]
# In[11]:
# Step 3. Calculate the passing percentages with the new total student count.
# In[12]:
# Calculate the students who passed both reading and math.
passing_math_reading = school_data_complete_df[(school_data_complete_df["math_score"] >= 70)
& (school_data_complete_df["reading_score"] >= 70)]
# Calculate the number of students that passed both reading and math.
overall_passing_math_reading_count = passing_math_reading["student_name"].count()
# Step 4.Calculate the overall passing percentage with new total student count.
# In[14]:
# Create a DataFrame
district_summary_df = pd.DataFrame(
[{"Total Schools": school_count,
"Total Students": student_count,
"Total Budget": total_budget,
"Average Math Score": average_math_score,
"Average Reading Score": average_reading_score,
"% Passing Math": passing_math_percentage,
"% Passing Reading": passing_reading_percentage,
"% Overall Passing": overall_passing_percentage}])
# Format the "Total Students" to have the comma for a thousands separator.
district_summary_df["Total Students"] = district_summary_df["Total Students"].map("{:,}".format)
# Format the "Total Budget" to have the comma for a thousands separator, a decimal separator and a "$".
district_summary_df["Total Budget"] = district_summary_df["Total Budget"].map("${:,.2f}".format)
# Format the columns.
district_summary_df["Average Math Score"] = district_summary_df["Average Math Score"].map("{:.1f}".format)
district_summary_df["Average Reading Score"] = district_summary_df["Average Reading Score"].map("{:.1f}".format)
district_summary_df["% Passing Math"] = district_summary_df["% Passing Math"].map("{:.1f}".format)
district_summary_df["% Passing Reading"] = district_summary_df["% Passing Reading"].map("{:.1f}".format)
district_summary_df["% Overall Passing"] = district_summary_df["% Overall Passing"].map("{:.1f}".format)
# Display the data frame
district_summary_df
# ## School Summary
# In[15]:
# Determine the School Type
per_school_types = school_data_df.set_index(["school_name"])["type"]
# Calculate the total student count.
per_school_counts = school_data_complete_df["school_name"].value_counts()
# Calculate the total school budget and per capita spending
per_school_budget = school_data_complete_df.groupby(["school_name"]).mean()["budget"]
# Calculate the per capita spending.
per_school_capita = per_school_budget / per_school_counts
# Calculate the average test scores.
per_school_math = school_data_complete_df.groupby(["school_name"]).mean()["math_score"]
per_school_reading = school_data_complete_df.groupby(["school_name"]).mean()["reading_score"]
# Calculate the passing scores by creating a filtered DataFrame.
per_school_passing_math = school_data_complete_df[(school_data_complete_df["math_score"] >= 70)]
per_school_passing_reading = school_data_complete_df[(school_data_complete_df["reading_score"] >= 70)]
# Calculate the number of students passing math and passing reading by school.
per_school_passing_math = per_school_passing_math.groupby(["school_name"]).count()["student_name"]
per_school_passing_reading = per_school_passing_reading.groupby(["school_name"]).count()["student_name"]
# Calculate the percentage of passing math and reading scores per school.
per_school_passing_math = per_school_passing_math / per_school_counts * 100
per_school_passing_reading = per_school_passing_reading / per_school_counts * 100
# Calculate the students who passed both reading and math.
per_passing_math_reading = school_data_complete_df[(school_data_complete_df["reading_score"] >= 70)
& (school_data_complete_df["math_score"] >= 70)]
# Calculate the number of students passing math and passing reading by school.
per_passing_math_reading = per_passing_math_reading.groupby(["school_name"]).count()["student_name"]
# Calculate the percentage of passing math and reading scores per school.
per_overall_passing_percentage = per_passing_math_reading / per_school_counts * 100
# In[16]:
# Create the DataFrame
per_school_summary_df = pd.DataFrame({
"School Type": per_school_types,
"Total Students": per_school_counts,
"Total School Budget": per_school_budget,
"Per Student Budget": per_school_capita,
"Average Math Score": per_school_math,
"Average Reading Score": per_school_reading,
"% Passing Math": per_school_passing_math,
"% Passing Reading": per_school_passing_reading,
"% Overall Passing": per_overall_passing_percentage})
# per_school_summary_df.head()
# In[17]:
# Format the Total School Budget and the Per Student Budget
per_school_summary_df["Total School Budget"] = per_school_summary_df["Total School Budget"].map("${:,.2f}".format)
per_school_summary_df["Per Student Budget"] = per_school_summary_df["Per Student Budget"].map("${:,.2f}".format)
# Display the data frame
per_school_summary_df
# In[18]:
# Step 5. Get the number of 10th-12th graders from Thomas High School (THS).
# In[19]:
# Step 6. Get all the students passing math from THS
# In[20]:
# Step 7. Get all the students passing reading from THS
# In[21]:
# Step 8. Get all the students passing math and reading from THS
# In[22]:
# Step 9. Calculate the percentage of 10th-12th grade students passing math from Thomas High School.
# In[23]:
# Step 10. Calculate the percentage of 10th-12th grade students passing reading from Thomas High School.
# In[24]:
# Step 11. Calculate the overall passing percentage of 10th-12th grade from Thomas High School.
# In[25]:
# Step 12. Replace the passing math percent for Thomas High School in the per_school_summary_df.
# In[26]:
# Step 13. Replace the passing reading percentage for Thomas High School in the per_school_summary_df.
# In[27]:
# Step 14. Replace the overall passing percentage for Thomas High School in the per_school_summary_df.
# In[28]:
# per_school_summary_df
# ## High and Low Performing Schools
# In[29]:
# Sort and show top five schools.
# In[30]:
# Sort and show top five schools.
# ## Math and Reading Scores by Grade
# In[31]:
# Create a Series of scores by grade levels using conditionals.
# Group each school Series by the school name for the average math score.
# Group each school Series by the school name for the average reading score.
# In[32]:
# Combine each Series for average math scores by school into single data frame.
# In[33]:
# Combine each Series for average reading scores by school into single data frame.
# In[34]:
# Format each grade column.
# In[35]:
# Remove the index.
# Display the data frame
# In[36]:
## Remove the index.
# Display the data frame
# ## Scores by School Spending
# In[37]:
# Establish the spending bins and group names.
# Categorize spending based on the bins.
# In[38]:
# Calculate averages for the desired columns.
# In[39]:
# Create the DataFrame
# In[40]:
# Format the DataFrame
# ## Scores by School Size
# In[41]:
# Establish the bins.
# Categorize spending based on the bins.
# In[42]:
# Calculate averages for the desired columns.
# In[43]:
# Assemble into DataFrame.
# In[44]:
# Format the DataFrame
# ## Scores by School Type
# In[45]:
# Calculate averages for the desired columns.
# In[46]:
# Assemble into DataFrame.
# In[47]:
# # Format the DataFrame
# In[ ]: