<jupyter_start><jupyter_text>2019 UTSW Salary Analysis<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
data = pd.read_excel('TPIA_Data_2019.xlsx')
data.head()
# Let's see how distrubution of full time vs part time looks
f1 = data['Full Time Type Description'].value_counts()
print(f1)

p_full = plt.figure()
pf1 = p_full.add_axes([0.1,0.1,0.8,0.8])
pf1.pie(f1.values.tolist(), labels=f1.index.values.tolist())
# Gender distribution
g1 = data['Gender'].value_counts()
print(g1)

p_gender = plt.figure()
ax1 = p_gender.add_axes([0.1,0.1,0.8,0.8])
ax1.pie(g1.values.tolist(), labels=g1.index.values.tolist())

data[data['Primary Name'].apply(lambda x: x.lower().find('maruni') >= 0)]
# Respiratory department job codes
data[data['Department Description'] == 'Respiratory Therapy']['Job Code Description'].value_counts()
data[data['Department Description'] == 'Respiratory Therapy']['Gender'].value_counts()
data[data['Department Description'] == 'Respiratory Therapy'][['Gender', 'Annual Pay']].groupby('Gender').describe()
data[['Department Description', 'Annual Pay']].groupby('Department Description').describe()
data[['Department Description', 'Gender' , 'Annual Pay']].groupby(['Department Description', 'Gender']).describe()
data[[ 'Gender' , 'Annual Pay']].groupby(['Gender']).describe()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1816860465116279, 'fraction_numerical': 0.02688953488372093, 'mean_word_length': 7.770700636942675, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '557388', 'license': 'MIT', 'n_tokens_mistral': 504, 'n_tokens_neox': 464, 'n_words': 95, 'path': 'projects/utsw2019/utsw_salaries_2019.ipynb', 'repo_name': 'tapiwam/dataSciProjects', 'size': '1376'}