CPI.Average: This is the average Consumer Price Index associated with that year. It was used to calculate
2018-equivalent values.
High.2018: This is the 2018-equivalent dollars for High.Value.
Low.2018: This is the 2018-equivalent dollars for Low.Value.
'''
# Displaying all columns without '...'
pd.options.display.width = 0
df = pd.read_csv('datasets/Minimum Wage Data.csv', encoding="latin")
df.to_csv('datasets/minwage.csv', encoding="utf-8")
df = pd.read_csv('datasets/minwage.csv')
hdr("df.head()")
print(df.head())
# Now we use group-by:
gb = df.groupby('State')
hdr("gb.get_group('Alabama').set_index('Year').head()")
print(gb.get_group('Alabama').set_index('Year').head())
# The above is the same as the following syntax, but using pandas instead of regular Python
# print(df[df['State'] == 'Alabama'].set_index('Year').head())
# Iterating over groups
act_min_wage = pd.DataFrame()
for name, group in df.groupby('State'):
import pandas as pd
import matplotlib.pyplot as plt
from helpers import hdr
# df is a dataframe
df = pd.read_csv("datasets/avocado.csv")
# First x rows
hdr("df.head(4)")
print(df.head(4))
# Last x rows
hdr("df.tail(5)")
print(df.tail(5))
# Get the column AveragePrice
hdr("df['AveragePrice'].head()")
print(df['AveragePrice'].head())
# The dot notation is uncommon! (df.AveragePrice.head())
# Get all rows where region == Albany
albany_df = df[df['region'] == "Albany"]
# The result is a new dataframe
hdr("albany_df.head()")
print(albany_df.head())
# Show the indexes of the dataframe
hdr("albany_df.index")
print(albany_df.index)
from helpers import hdr
pd.options.display.width = 0
df = pd.read_csv('datasets/minwage.csv')
act_min_wage = pd.DataFrame()
for name, group in df.groupby('State'):
if act_min_wage.empty:
act_min_wage = group.set_index('Year')[['Low.2018']].rename(columns={'Low.2018':name})
else:
act_min_wage = act_min_wage.join(group.set_index('Year')[['Low.2018']].rename(columns={'Low.2018':name}))
hdr('act_min_wage.head()')
print(act_min_wage.head())
min_wage_corr = act_min_wage.replace(0, np.NaN).dropna(axis=1).corr()
hdr('min_wage_corr.head()')
print(min_wage_corr.head())
plt.matshow(min_wage_corr)
plt.show()
labels = [c[:2].upper() for c in min_wage_corr.columns]
def show_heatmap(labels):
fig = plt.figure(figsize=(12, 12))
# <- all the subplots in the figure are in a 1 by 1 grid, and this is number one (only 1 graph)
import pandas as pd
import matplotlib.pyplot as plt
from helpers import hdr
df = pd.read_csv("datasets/avocado.csv")
# Convert the Date column to datetime objects
df['Date'] = pd.to_datetime(df['Date'])
hdr("df.head()")
print(df.head())
albany_df = df.copy()[df['region'] == "Albany"]
albany_df.set_index("Date", inplace=True)
albany_df["AveragePrice"].plot()
plt.show()
# Moving average of size 25
albany_df["AveragePrice"].rolling(25).mean().plot()
plt.show()
# It doesn't look right.
# We need to sort the dataset by the Date, which is the index:
albany_df.sort_index(inplace=True)
albany_df["AveragePrice"].rolling(25).mean().plot(
title="AveragePrice with rolling average=25")
plt.show()
# Put this data as a new column in our dataframe:
import pandas as pd
from helpers import hdr
pd.options.display.width = 0
df = pd.read_csv("datasets/diamonds.csv", index_col=0)
hdr("df.head()")
print(df.head())
# Starting with linear regression
# It would be ideal that our string classifications are linear, meaning they have a meaningful order.
# Let's see what all of our cuts are, for example
hdr("df['cut'].unique()")
print(df['cut'].unique())
# Now we hard-code the order:
cut_class_dict = {
'Fair': 1,
'Good': 2,
'Very Good': 3,
'Premium': 4,
'Ideal': 5
}
# Let's see clarity
hdr("df['clarity'].unique()")
print(df['clarity'].unique())
clarity_dict = {
"I3": 1,
import pandas as pd
import numpy as np
from helpers import hdr
pd.options.display.width = 0
# In this lesson we'll try to find correlation between unemployment and the minimum wage
unemp_county = pd.read_csv('datasets/unemployment-by-county-us/output.csv')
hdr('unemp_county.head()')
print(unemp_county.head())
df = pd.read_csv('datasets/minwage.csv')
act_min_wage = pd.DataFrame()
for name, group in df.groupby('State'):
if act_min_wage.empty:
act_min_wage = group.set_index('Year')[[
'Low.2018'
]].rename(columns={'Low.2018': name})
else:
act_min_wage = act_min_wage.join(
group.set_index('Year')[['Low.2018'
]].rename(columns={'Low.2018': name}))
hdr('act_min_wage.head()')
print(act_min_wage.head())