Deep Learning Python
Pandas Usage
#pandas
Generating Dataframe from CSV
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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt # For data visualization
df = pd.read_csv('bear.csv')
df.head()
Processing
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print(df.info())
Distribution
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### Showing historgram
import matplotlib.pyplot as plt
print(df['tune'])
df['tune'].plot(kind='hist', bins=1000, title='Distribution of `tune` Feature')
plt.xlabel('Tune Value')
plt.ylabel('Frequency')
plt.show()
### Bar bart showing the number of occrences
df['bear'].value_counts().plot(kind='barh', title='Frequency of Bear Types')
plt.xlabel('Number of Occrences')
plt.ylabel('Bear Type')
plt.show()
### List unique data
unique_values = df['val'].unique()
print("Unique characters in the 'val' column:", unique_values)
Mean
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# Group by the val type and aggregate to the average `tune` value
mean_tunes = df.groupby('val')['tune'].mean()
print(mean_tunes)
#Group by bear type to teh average 'tune'
mean_tunes = df.groupby('bear')['tune'].mean()
print(mean_tunes)
Filtering
Generating new DF from original DF with a filter
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# Filtering to find entries where 'tune' values are above a certain threshold
high_tune_bears = df[df['tune'] > 90]
print(high_tune_bears.head(5))
# Applying multiple conditions to find a specific subset of data
specific_bears = df[(df['tune'] > 50) & (df['bear'] == 'Kodiak')]
print(specific_bears.head(5))
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CC BY 4.0
by the author.