Xuliqin Practice1114

%pip install openpyxl
Requirement already satisfied: openpyxl in d:\program files\python3.12.7\lib\site-packages (3.1.5)
Requirement already satisfied: et-xmlfile in d:\program files\python3.12.7\lib\site-packages (from openpyxl) (2.0.0)
Note: you may need to restart the kernel to use updated packages.

[notice] A new release of pip is available: 24.2 -> 24.3.1
[notice] To update, run: python.exe -m pip install --upgrade pip
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
import pingouin as pg
from lets_plot import *

LetsPlot.setup_html(no_js=True)

### You don't need to use these settings yourself
### — they are just here to make the book look nicer!
# Set the plot style for prettier charts:
plt.style.use("https://raw.githubusercontent.com/aeturrell/core_python/main/plot_style.txt")
data_np = pd.read_excel(
    "./file/doing-economics-datafile-working-in-excel-project-2.xls",
    usecols="A:Q",
    header=1,
    index_col="Period",
)
data_n = data_np.iloc[:10, :].copy()
data_p = data_np.iloc[14:24, :].copy()
D:\Program Files\python3.12.7\Lib\site-packages\openpyxl\worksheet\_reader.py:329: UserWarning: Unknown extension is not supported and will be removed
  warn(msg)
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
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

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
data_n = data_n.astype("double")
data_p = data_p.astype("double")
mean_n_c = data_n.mean(axis=1)
mean_p_c = data_p.agg(np.mean, axis=1)
C:\Users\zhangyongtian\AppData\Local\Temp\ipykernel_7132\3801786469.py:2: FutureWarning: The provided callable <function mean at 0x0000028CC7F3D260> 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)
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()

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

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

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()

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
# 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)
50
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)
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()

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"})
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 
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 
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 
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