Financial Data Science Financial Performance Analysis: Difference between revisions

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# load the DataFrame
# load the DataFrame
df = pd.read_csv("./data.csv", parse_dates=["date"])
df = pd.read_csv("./finances.csv", parse_dates=["date"])


# make it a time series DataFrame
# make it a time series DataFrame

Revision as of 22:55, 20 October 2023

Internal

Overview

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.set_index('date')

# declare the function that converts the dollar amount
def dollar_to_int(s: str):
    return int(s[1:].replace(',',''))

# extract a specific time series and plot it
fidelity_self_managed = df[''].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()