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Merged
mmcky merged 1 commit into from
Aug 18, 2026
Merged

Read the three wave-C2 datasets from data-lectures (Track C) #375

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6 changes: 3 additions & 3 deletions lectures/five_preferences.md
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,7 @@ import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rc
from scipy import optimize, stats
from scipy.io import loadmat
import pandas as pd
from matplotlib.collections import LineCollection
from numba import njit
```
Expand Down Expand Up @@ -1845,7 +1845,7 @@ aversion associated with a logarithmic one-period utility function.
:tags: [hide-input]

# Load data
data = loadmat('dataBHS.mat')
data = pd.read_csv('https://github.com/QuantEcon/data-lectures/raw/main/lectures/dataBHS.csv')

# Set parameter values
μ_c = 0.004952
Expand All @@ -1857,7 +1857,7 @@ data = loadmat('dataBHS.mat')
:tags: [hide-input]

# Compute consumption growth
c = data['c']
c = data[['c']].to_numpy() # keep the (236, 1) column shape of the source arrays
c_growth = c[1:] - c[:-1]

# Create histogram of consumption growth
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4 changes: 2 additions & 2 deletions lectures/match_transport.md
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Expand Up @@ -2231,8 +2231,8 @@ Then we sort occupations by average log-earnings within each occupation.
The resulting dataset is included in the dataset `acs_data_summary.csv`

```{code-cell} ipython3
data_path = '_static/lecture_specific/match_transport/'
occupation_df = pd.read_csv(data_path + 'acs_data_summary.csv')
data_url = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/'
occupation_df = pd.read_csv(data_url + 'acs_data_summary.csv')
```

+++ {"user_expressions": []}
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3 changes: 1 addition & 2 deletions lectures/risk_aversion_or_mistaken_beliefs.md
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Expand Up @@ -1611,8 +1611,7 @@ mystnb:
name: fig-us-yields
---
data = pd.read_csv(
'https://raw.githubusercontent.com/QuantEcon/lecture-python-advanced.myst/refs/heads/'
'main/lectures/_static/lecture_specific/risk_aversion_or_mistaken_beliefs/fred_data.csv',
'https://github.com/QuantEcon/data-lectures/raw/main/lectures/fred_data.csv',
parse_dates=['DATE'], index_col='DATE'
)

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