Additional Pandas Functionality

Additional things you can do with pandas that wasn't mentioned in the webinar.

Setup



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import pandas as pd
import numpy as np



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df = pd.read_csv("./data_cleaned.csv")
df.head()



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df["Sales ID"] = df.index + 1
df.head()



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df.pivot_table(index="Sales Person")



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df.groupby("Sales Person").sum()



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df.pivot_table(index=["Sales Person", "Order Date"])



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df_quarter = pd.read_csv("./data_quarter.csv")
df_quarter



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df = df.merge(df_quarter, on="Sales ID")
df



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df["Sales Person"].unique()



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pd.unique([1, 1, 2, 3, 7, 9, 2, 1])



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df.query("Quarter == 4")



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df[df.Quarter == 4]



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columns = df.columns.map(lambda col: col.replace(' ', '_'))
df.columns = columns
df



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df.query('Items_Sold > 1 and Quarter == 2')



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df.query("(Sales_Person == 'John' or Sales_Person == 'Mary') and Item_Price > 5")