Video url: https://www.youtube.com/watch?v=vmEHCJofslg
Pandas document: https://pandas.pydata.org/pandas-docs/stable/reference/index.html#api
Pracice Pandas: https://stratascratch.com/?via=keith
1. Loading data into Pandas
1.1 Loading data from csv format file
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df = pd.read_csv('pokemon_data.csv')
print(df.head(3))
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1.2 Loading data from txt format file
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df_txt = pd.read_csv('pokemon_data.txt', delimiter='\t')
print(df_txt.head(3))
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2. Read Data in Pandas
2.1 Read head
2.2 Read each Column
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| print(df['Name'][0:5])
print(df.Name[0:5])
print(df[['Name', 'Type 1', 'HP']][0:5])
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2.3 Read each Row
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print(df.head(4))
print(df.iloc[0])
for index, row in df.iterrows():
print(index, row['Name'])
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2.4 Read a specific location(R,C)
3. Sorting/Describing Data
3.1 View data describe
3.2 Sorting
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| sort_res = df.sort_values(['Name','HP'], ascending=[1, 0])
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4. Making changes to the data
4.1 Create a new column
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| df['Total'] = df['HP'] + df['Attack']
df['Total'] = df.iloc[:,4:10].sum(axis=1)
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4.2 Delete one column
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| df = df.drop(columns=['Total'])
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5. Save data to file
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| df.to_csv('new_data.csv', index=False, sep='\t')
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6. Filtering Data
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new_df = df.loc[(df['Type 1'] == 'Grass') & (df['Type 2'] == 'Poison')]
new_df.reset_index(drop=True, inplace=True)
new_df = df.loc[~df['Name'].str.contains('Mega')]
df.loc[df['Type 1'].str.contains('fire|Grass',flags=re.I, regex=True)]
df.loc[df['Name'].str.contains('^pi[a-z]*', flags=re.I, regex=True)]
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7. Conditional changes
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df.loc[df['Type 1'] == 'Fire', 'Type 1'] = 'Flamer'
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8. Aggregate Statistics(Groupby)
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| res = df.groupby(['Type 1']).mean().sort_values('HP', ascending=False)
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9. Working with large amounts of data
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for df in pd.read_csv('pokemon_data.csv', chunk_size=100):
print(df)
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