285 KiB
285 KiB
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Exercise 1.1¶
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import pandas as pd
# Import built-in libraries 'string' and 'random'
import random
import string
# Get upper- and lowercase letters from 'string' library
lower = string.ascii_lowercase
upper = string.ascii_uppercase
# Create a dictionary with dummy data of integers and letters
d = {'Integers': [random.randint(1, 100) for i in range(1, 100)],
'Lowercase': [random.choice(lower) for i in range(1, 100)],
'Uppercase': [random.choice(upper) for i in range(1, 100)]}
# Create dataframe from dictionary
df = pd.DataFrame(d)
# df # <- This would print all 100 rows
Exercise 1.2¶
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df.head(3)
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df.tail(4)
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Exercise 1.3¶
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dfk = df[df['Uppercase'] == 'K']
dfk.head()
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Exercise 1.4¶
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dfk = dfk.reset_index(drop=True)
dfk
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Exercise 2.1¶
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# Import csv file to dataframe
dfr = pd.read_csv('shear_key_forces.csv')
dfr.head()
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Exercise 2.2¶
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# Filer dataframe to contain only shear keys
dfr = dfr[dfr['Shear_key'] != 'Not_a_shear_key']
dfr.head()
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Exercise 2.3¶
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dfr = dfr.drop('Element_no', axis=1)
dfr.head()
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Exercise 2.4¶
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# Create groupby object and perform sum operation on it
dfr_sum = dfr.groupby(['Shear_key', 'LC', 'LC-title'], as_index=False).sum()
dfr_sum.head()
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The list of column labels ['Shear_key', 'LC', 'LC-title'] are the columns that are to be present in the resulting dataframe. The parameter as_index is by default True, which would create a multiindex out of all the column labels listed. By setting as_index=False, we reset to 0-indexing.
Exercise 2.5¶
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# Filter for shear key 1
df_key1 = dfr_sum[dfr_sum['Shear_key'] == 'Shear_key1']
# Use built-in pandas plot
df_key1.plot(kind='bar', x='LC', y='P[kN]', rot=45, figsize=(15, 5), color='orange', alpha=0.50)
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Note: The same plot could be constructed by extracting arrays in columns 'LC' and 'P[kN]' to variables and using matplotlib itself as plt.bar(...). To do that, you would have to import matplotlib.pyplot as plt.
pandas just provides a shortcut to get a fast graph out.
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