Financial Data Science Financial Performance Analysis: Difference between revisions
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Line 17: | Line 17: | ||
# declare the function that converts the dollar amount | # declare the function that converts the dollar amount | ||
def dollar_to_int(s: str): | def dollar_to_int(s): | ||
if isinstance(s, str): | |||
return int(s[1:].replace(',','')) | |||
elif math.isnan(s): | |||
return 0 | |||
else: | |||
return -1 | |||
# extract a specific time series and plot it | # extract a specific time series and plot it |
Revision as of 23:14, 20 October 2023
Internal
Overview
import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as ptick
# load the DataFrame
df = pd.read_csv("./finances.csv", parse_dates=["Date"])
# make it a time series DataFrame
df = df.set_index('Date')
# declare the function that converts the dollar amount
def dollar_to_int(s):
if isinstance(s, str):
return int(s[1:].replace(',',''))
elif math.isnan(s):
return 0
else:
return -1
# extract a specific time series and plot it
fidelity_self = df['Fidelity Self'].apply(dollar_to_int)
# graph
fig, ax = plt.subplots()
fig.autofmt_xdate()
ax.set_ylabel("amount")
ax.yaxis.set_major_formatter(mt.FormatStrFormatter('% 1.2f'))
ax.plot(fidelity_self_managed)
plt.show()