In [1]:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
In [14]:
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")
Out[14]:
<AxesSubplot:title={'center':'Mean Total Duration'}, xlabel='game'>
In [15]:
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
In [16]:
#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
In [ ]:
 
In [2]:
# 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))
Out[2]:
<AxesSubplot:xlabel='game'>