import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv("BarPlotsMeanI.csv")
df.plot(x="game", y="percentage_correct_responses", kind="bar", title="Mean Percentage of Correct Responses",
color=["red", "red", "dodgerblue", "dodgerblue", "darkorange", "darkorange", "limegreen", "limegreen", "deeppink", "deeppink"])
df.plot(x="game", y="commision_errors", kind="bar", color=["red", "red", "dodgerblue", "dodgerblue", "darkorange", "darkorange", "limegreen", "limegreen", "deeppink", "deeppink"], title="Mean Commission Errors")
df.plot(x="game", y="omission_errors", kind="bar", color=["red", "red", "dodgerblue", "dodgerblue", "darkorange", "darkorange", "limegreen", "limegreen", "deeppink", "deeppink"], title="Mean Omission Errors")
df.plot(x="game", y="mean_reaction_time", kind="bar", color=["red", "red", "dodgerblue", "dodgerblue", "darkorange", "darkorange", "limegreen", "limegreen", "deeppink", "deeppink"], title="Mean Reaction Time")
df.plot(x="game", y="total_duration", kind="bar", color=["red", "red", "dodgerblue", "dodgerblue", "darkorange", "darkorange", "limegreen", "limegreen", "deeppink", "deeppink"], title="Mean Total Duration")
<AxesSubplot:title={'center':'Mean Total Duration'}, xlabel='game'>
df = pd.read_csv("BarPlotsMean.csv")
display(df)
df1 = df["game"]
#display(df1)
df = df.drop(df.columns[0], axis=1)
#display(df)
# copy the data
df_min_max_scaled = df.copy()
# apply normalization techniques
for column in df_min_max_scaled.columns:
df_min_max_scaled[column] = (df_min_max_scaled[column] - df_min_max_scaled[column].min()) / (df_min_max_scaled[column].max() - df_min_max_scaled[column].min())
#view normalized data
display(df_min_max_scaled)
df2 = df_min_max_scaled
game | age | percentage_correct_responses | commision_errors | omission_errors | mean_reaction_time | total_duration | no_of_records | total_records | |
---|---|---|---|---|---|---|---|---|---|
0 | Alternating | 4 | 91.866029 | 2.090909 | 1.545455 | 1409.727273 | 57000.00000 | 11 | NaN |
1 | Divided | 4 | 79.545455 | 2.727273 | 1.636364 | 1203.363636 | 70000.00000 | 11 | NaN |
2 | Sustained | 4 | 87.816529 | 0.000000 | 1.875000 | 1410.625000 | 104740.25000 | 8 | NaN |
3 | Focused | 4 | 93.076923 | 0.000000 | 0.692308 | 1416.346154 | 73807.69231 | 26 | NaN |
4 | Selective | 4 | 93.463203 | 0.290909 | 0.454545 | 0.000000 | 12729.12727 | 55 | 111.0 |
5 | Alternating | 5 | 93.301435 | 3.363636 | 1.272727 | 1236.909091 | 57000.00000 | 11 | NaN |
6 | Divided | 5 | 75.000000 | 2.444444 | 2.000000 | 1130.666667 | 60000.00000 | 9 | NaN |
7 | Sustained | 5 | 85.915966 | 0.000000 | 1.400000 | 1081.000000 | 98201.00000 | 5 | NaN |
8 | Focused | 5 | 96.153846 | 0.000000 | 0.461538 | 1432.384615 | 89173.07692 | 26 | NaN |
9 | Selective | 5 | 95.502645 | 0.240741 | 0.277778 | 0.000000 | 13206.27778 | 54 | 105.0 |
age | percentage_correct_responses | commision_errors | omission_errors | mean_reaction_time | total_duration | no_of_records | total_records | |
---|---|---|---|---|---|---|---|---|
0 | 0.0 | 0.797303 | 0.621622 | 0.736070 | 0.984182 | 0.481147 | 0.12 | NaN |
1 | 0.0 | 0.214876 | 0.810811 | 0.788856 | 0.840112 | 0.622434 | 0.12 | NaN |
2 | 0.0 | 0.605872 | 0.000000 | 0.927419 | 0.984809 | 1.000000 | 0.06 | NaN |
3 | 0.0 | 0.854545 | 0.000000 | 0.240695 | 0.988803 | 0.663817 | 0.42 | NaN |
4 | 0.0 | 0.872806 | 0.086486 | 0.102639 | 0.000000 | 0.000000 | 1.00 | 1.0 |
5 | 1.0 | 0.865159 | 1.000000 | 0.577713 | 0.863531 | 0.481147 | 0.12 | NaN |
6 | 1.0 | 0.000000 | 0.726727 | 1.000000 | 0.789360 | 0.513752 | 0.08 | NaN |
7 | 1.0 | 0.516028 | 0.000000 | 0.651613 | 0.754686 | 0.928930 | 0.00 | NaN |
8 | 1.0 | 1.000000 | 0.000000 | 0.106700 | 1.000000 | 0.830812 | 0.42 | NaN |
9 | 1.0 | 0.969216 | 0.071572 | 0.000000 | 0.000000 | 0.005186 | 0.98 | 0.0 |
#display(df1)
#display(df2)
result0 = pd.concat([df1, df2], axis=1, join='inner')
#display(result0)
result1 = result0.iloc[0:5]
display(result1)
figure = result1.plot(width=0.7, x="game", y=["percentage_correct_responses", "commision_errors", "omission_errors", "mean_reaction_time", "total_duration"], kind="bar", figsize=(25, 5), title="Age 4", color=["red", "dodgerblue", "darkorange", "limegreen", "deeppink"])
result2 = result0.iloc[5:11]
display(result2)
figure = result2.plot(width=0.7, x="game", y=["percentage_correct_responses", "commision_errors", "omission_errors", "mean_reaction_time", "total_duration"], kind="bar", figsize=(25, 5), title="Age 5", color=["red", "dodgerblue", "darkorange", "limegreen", "deeppink"])
game | age | percentage_correct_responses | commision_errors | omission_errors | mean_reaction_time | total_duration | no_of_records | total_records | |
---|---|---|---|---|---|---|---|---|---|
0 | Alternating | 0.0 | 0.797303 | 0.621622 | 0.736070 | 0.984182 | 0.481147 | 0.12 | NaN |
1 | Divided | 0.0 | 0.214876 | 0.810811 | 0.788856 | 0.840112 | 0.622434 | 0.12 | NaN |
2 | Sustained | 0.0 | 0.605872 | 0.000000 | 0.927419 | 0.984809 | 1.000000 | 0.06 | NaN |
3 | Focused | 0.0 | 0.854545 | 0.000000 | 0.240695 | 0.988803 | 0.663817 | 0.42 | NaN |
4 | Selective | 0.0 | 0.872806 | 0.086486 | 0.102639 | 0.000000 | 0.000000 | 1.00 | 1.0 |
game | age | percentage_correct_responses | commision_errors | omission_errors | mean_reaction_time | total_duration | no_of_records | total_records | |
---|---|---|---|---|---|---|---|---|---|
5 | Alternating | 1.0 | 0.865159 | 1.000000 | 0.577713 | 0.863531 | 0.481147 | 0.12 | NaN |
6 | Divided | 1.0 | 0.000000 | 0.726727 | 1.000000 | 0.789360 | 0.513752 | 0.08 | NaN |
7 | Sustained | 1.0 | 0.516028 | 0.000000 | 0.651613 | 0.754686 | 0.928930 | 0.00 | NaN |
8 | Focused | 1.0 | 1.000000 | 0.000000 | 0.106700 | 1.000000 | 0.830812 | 0.42 | NaN |
9 | Selective | 1.0 | 0.969216 | 0.071572 | 0.000000 | 0.000000 | 0.005186 | 0.98 | 0.0 |
# df = df.iloc[1:6]
df.plot(x="game", y=["percentage_correct_responses", "commision_errors", "omission_errors", "mean_reaction_time", "total_duration"],
kind="bar",
color=["red", "dodgerblue", "darkorange", "limegreen", "deeppink"])
#figsize=(15, 5))
<AxesSubplot:xlabel='game'>