fba8e3b056425013930abb11e0a970

In [3]:

import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
from lets_plot import *


LetsPlot.setup_html(no_js=True)

In [4]:

plt.style.use(

    "https://raw.githubusercontent.com/aeturrell/core_python/main/plot_style.txt"
)

In [5]:

data_np = pd.read_excel(
    "data/doing-economics-datafile-working-in-excel-project-2.xlsx",
    usecols="A:Q",
    header=1,
    index_col="Period",
)
data_n = data_np.iloc[:10, :].copy()
data_p = data_np.iloc[14:24, :].copy()

c:\Users\HP\anaconda3\Lib\site-packages\openpyxl\worksheet\_read_only.py:85: UserWarning: Unknown extension is not supported and will be removed
  for idx, row in parser.parse():

In [6]:

test_data = {
    "City A": [14.1, 14.1, 13.7],
    "City B": [11.0, 12.6, 12.1],
}


# Original dataframe
test_df = pd.DataFrame.from_dict(test_data)
# A copy of the dataframe
test_copy = test_df.copy()
# A pointer to the dataframe
test_pointer = test_df


test_pointer.iloc[1, 1] = 99

In [7]:

print("test_df=")
print(f"{test_df}\n")
print("test_copy=")
print(f"{test_copy}\n")

test_df=
   City A  City B
0    14.1    11.0
1    14.1    99.0
2    13.7    12.1

test_copy=
   City A  City B
0    14.1    11.0
1    14.1    12.6
2    13.7    12.1

In [8]:

data_n.info()

<class 'pandas.core.frame.DataFrame'>
Index: 10 entries, 1 to 10
Data columns (total 16 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   Copenhagen       10 non-null     object
 1   Dnipropetrovs’k  10 non-null     object
 2   Minsk            10 non-null     object
 3   St. Gallen       10 non-null     object
 4   Muscat           10 non-null     object
 5   Samara           10 non-null     object
 6   Zurich           10 non-null     object
 7   Boston           10 non-null     object
 8   Bonn             10 non-null     object
 9   Chengdu          10 non-null     object
 10  Seoul            10 non-null     object
 11  Riyadh           10 non-null     object
 12  Nottingham       10 non-null     object
 13  Athens           10 non-null     object
 14  Istanbul         10 non-null     object
 15  Melbourne        10 non-null     object
dtypes: object(16)
memory usage: 1.3+ KB

In [9]:

data_n = data_n.astype("double")
data_p = data_p.astype("double")

In [10]:

mean_n_c = data_n.mean(axis=1)
mean_p_c = data_p.agg(np.mean, axis=1)

C:\Users\HP\AppData\Local\Temp\ipykernel_16488\3801786469.py:2: FutureWarning: The provided callable <function mean at 0x000002B530AE80E0> is currently using DataFrame.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
  mean_p_c = data_p.agg(np.mean, axis=1)

In [11]:

fig, ax = plt.subplots()
mean_n_c.plot(ax=ax, label="Without punishment")
mean_p_c.plot(ax=ax, label="With punishment")
ax.set_title("Average contributions to the public goods game")
ax.set_ylabel("Average contribution")
ax.legend();

In [12]:

partial_names_list = ["F. Kennedy", "Lennon", "Maynard Keynes", "Wayne"]
["John " + name for name in partial_names_list]

['John F. Kennedy', 'John Lennon', 'John Maynard Keynes', 'John Wayne']

In [13]:

# Create new dataframe with bars in
compare_grps = pd.DataFrame(
    [mean_n_c.loc[[1, 10]], mean_p_c.loc[[1, 10]]],
    index=["Without punishment", "With punishment"],
)
# Rename columns to have 'round' in them
compare_grps.columns = ["Round " + str(i) for i in compare_grps.columns]
# Swap the column and index variables around with the transpose function, ready for plotting (.T is transpose)
compare_grps = compare_grps.T
# Make a bar chart
compare_grps.plot.bar(rot=0);

In [14]:

n_c = data_n.agg(["std", "var", "mean"], 1)
n_c

	std	var	mean
Period
1	2.020724	4.083325	10.578313
2	2.238129	5.009220	10.628398
3	2.329569	5.426891	10.407079
4	2.068213	4.277504	9.813033
5	2.108329	4.445049	9.305433
6	2.240881	5.021549	8.454844
7	2.136614	4.565117	7.837568
8	2.349442	5.519880	7.376388
9	2.413845	5.826645	6.392985
10	2.187126	4.783520	4.383769

In [15]:

p_c = data_p.agg(["std", "var", "mean"], 1)

In [16]:

fig, ax = plt.subplots()
n_c["mean"].plot(ax=ax, label="mean")
# mean + 2 standard deviations
(n_c["mean"] + 2 * n_c["std"]).plot(ax=ax, ylim=(0, None), color="red", label="±2 s.d.")
# mean - 2 standard deviations
(n_c["mean"] - 2 * n_c["std"]).plot(ax=ax, ylim=(0, None), color="red", label="")
for i in range(len(data_n.columns)):
    ax.scatter(x=data_n.index, y=data_n.iloc[:, i], color="k", alpha=0.3)
ax.legend()
ax.set_ylabel("Average contribution")
ax.set_title("Contribution to public goods game without punishment")
plt.show();

In [17]:

fig, ax = plt.subplots()
p_c["mean"].plot(ax=ax, label="mean")
# mean + 2 sd
(p_c["mean"] + 2 * p_c["std"]).plot(ax=ax, ylim=(0, None), color="red", label="±2 s.d.")
# mean - 2 sd
(p_c["mean"] - 2 * p_c["std"]).plot(ax=ax, ylim=(0, None), color="red", label="")
for i in range(len(data_p.columns)):
    ax.scatter(x=data_p.index, y=data_p.iloc[:, i], color="k", alpha=0.3)
ax.legend()
ax.set_ylabel("Average contribution")
ax.set_title("Contribution to public goods game with punishment")
plt.show();

In [18]:

data_p.apply(lambda x: x.max() - x.min(), axis=1)

Period
1     10.199675
2     12.185065
3     12.689935
4     12.625000
5     12.140375
6     12.827541
7     13.098931
8     13.482621
9     13.496754
10    11.307360
dtype: float64

In [19]:

# A lambda function accepting three inputs, a, b, and c, and calculating the sum of the squares
test_function = lambda a, b, c: a**2 + b**2 + c**2


# Now we apply the function by handing over (in parenthesis) the following inputs: a=3, b=4 and c=5
test_function(3, 4, 5)

In [20]:

range_function = lambda x: x.max() - x.min()
range_p = data_p.apply(range_function, axis=1)
range_n = data_n.apply(range_function, axis=1)

In [21]:

fig, ax = plt.subplots()
range_p.plot(ax=ax, label="With punishment")
range_n.plot(ax=ax, label="Without punishment")
ax.set_ylim(0, None)
ax.legend()
ax.set_title("Range of contributions to the public goods game")
plt.show();

In [22]:

funcs_to_apply = [range_function, "max", "min", "std", "mean"]
summ_p = data_p.apply(funcs_to_apply, axis=1).rename(columns={"<lambda>": "range"})
summ_n = data_n.apply(funcs_to_apply, axis=1).rename(columns={"<lambda>": "range"})

In [23]:

summ_n.loc[[1, 10], :].round(2)

	range	max	min	std	mean
Period
1	6.14	14.10	7.96	2.02	10.58
10	7.38	8.68	1.30	2.19	4.38

In [24]:

summ_p.loc[[1, 10], :].round(2)

	range	max	min	std	mean
Period
1	10.20	16.02	5.82	3.21	10.64
10	11.31	17.51	6.20	3.90	12.87

In [27]:

%pip install pingouin

Collecting pingouin
  Downloading pingouin-0.5.5-py3-none-any.whl.metadata (19 kB)
Requirement already satisfied: matplotlib in c:\users\hp\anaconda3\lib\site-packages (from pingouin) (3.9.2)
Requirement already satisfied: numpy in c:\users\hp\anaconda3\lib\site-packages (from pingouin) (1.26.4)
Requirement already satisfied: pandas>=1.5 in c:\users\hp\anaconda3\lib\site-packages (from pingouin) (2.2.2)
Collecting pandas-flavor (from pingouin)
  Downloading pandas_flavor-0.6.0-py3-none-any.whl.metadata (6.3 kB)
Requirement already satisfied: scikit-learn>=1.2 in c:\users\hp\anaconda3\lib\site-packages (from pingouin) (1.5.1)
Requirement already satisfied: scipy in c:\users\hp\anaconda3\lib\site-packages (from pingouin) (1.13.1)
Requirement already satisfied: seaborn in c:\users\hp\anaconda3\lib\site-packages (from pingouin) (0.13.2)
Requirement already satisfied: statsmodels in c:\users\hp\anaconda3\lib\site-packages (from pingouin) (0.14.2)
Requirement already satisfied: tabulate in c:\users\hp\anaconda3\lib\site-packages (from pingouin) (0.9.0)
Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\hp\anaconda3\lib\site-packages (from pandas>=1.5->pingouin) (2.9.0.post0)
Requirement already satisfied: pytz>=2020.1 in c:\users\hp\anaconda3\lib\site-packages (from pandas>=1.5->pingouin) (2024.1)
Requirement already satisfied: tzdata>=2022.7 in c:\users\hp\anaconda3\lib\site-packages (from pandas>=1.5->pingouin) (2023.3)
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Requirement already satisfied: threadpoolctl>=3.1.0 in c:\users\hp\anaconda3\lib\site-packages (from scikit-learn>=1.2->pingouin) (3.5.0)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\hp\anaconda3\lib\site-packages (from matplotlib->pingouin) (1.2.0)
Requirement already satisfied: cycler>=0.10 in c:\users\hp\anaconda3\lib\site-packages (from matplotlib->pingouin) (0.11.0)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\hp\anaconda3\lib\site-packages (from matplotlib->pingouin) (4.51.0)
Requirement already satisfied: kiwisolver>=1.3.1 in c:\users\hp\anaconda3\lib\site-packages (from matplotlib->pingouin) (1.4.4)
Requirement already satisfied: packaging>=20.0 in c:\users\hp\anaconda3\lib\site-packages (from matplotlib->pingouin) (24.1)
Requirement already satisfied: pillow>=8 in c:\users\hp\anaconda3\lib\site-packages (from matplotlib->pingouin) (10.4.0)
Requirement already satisfied: pyparsing>=2.3.1 in c:\users\hp\anaconda3\lib\site-packages (from matplotlib->pingouin) (3.1.2)
Requirement already satisfied: xarray in c:\users\hp\anaconda3\lib\site-packages (from pandas-flavor->pingouin) (2023.6.0)
Requirement already satisfied: patsy>=0.5.6 in c:\users\hp\anaconda3\lib\site-packages (from statsmodels->pingouin) (0.5.6)
Requirement already satisfied: six in c:\users\hp\anaconda3\lib\site-packages (from patsy>=0.5.6->statsmodels->pingouin) (1.16.0)
Downloading pingouin-0.5.5-py3-none-any.whl (204 kB)
Downloading pandas_flavor-0.6.0-py3-none-any.whl (7.2 kB)
Installing collected packages: pandas-flavor, pingouin
Successfully installed pandas-flavor-0.6.0 pingouin-0.5.5
Note: you may need to restart the kernel to use updated packages.

In [28]:

import pingouin as pg

In [29]:

pg.ttest(x=data_n.iloc[0, :], y=data_p.iloc[0, :])

	T	dof	alternative	p-val	CI95%	cohen-d	BF10	power
T-test	-0.063782	30	two-sided	0.949567	[-2.0, 1.87]	0.02255	0.337	0.050437

In [30]:

pg.ttest(x=data_n.iloc[0, :], y=data_p.iloc[0, :], paired=True)

	T	dof	alternative	p-val	CI95%	cohen-d	BF10	power
T-test	-0.149959	15	two-sided	0.882795	[-0.92, 0.8]	0.02255	0.258	0.05082