Preparing listed-company financial statement data on net profit and total assets with Exploratory Data Analysis (EDA)

Project info

• Field of studyEconomics and Finance
• LevelUndergraduate thesis (S1)
• Method typeData Quality
• Estimated build time± 3 working days
• Toolspandas describe, isna, corr; matplotlib hist, boxplot, imshow
• PriceUS$16 - US$100

Research goal

Apply Exploratory Data Analysis (EDA) to synthetic data to prepare listed-company financial statement data on net profit and total assets for further analysis.

Variables

Example result

• 284Observations
• 209.575Mean
• 23.298Std. deviation
• 1.917Skewness
• 5.115Excess kurtosis
• 0.496Max |correlation|

EDA of 284 observations: Net profit averages 209.57 (SD 23.30) with skewness 1.917; the strongest pairwise correlation is 0.496.

Computed from illustrative synthetic data, not from a real study.

About Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) summarises and visualises data to understand distributions, relations, patterns, and quality issues before modelling. It is always an early step in the methodology or results chapter of a data-based thesis.

Steps

1. 1Load the data and check dimensions and variable types
2. 2Compute descriptive statistics and missing counts
3. 3Visualise each variable's distribution (histogram, boxplot)
4. 4Compute and visualise correlations
5. 5Check outliers and class imbalance
6. 6Note findings that affect modelling steps

Assumptions to check

• The data context is understood so units and variable types are clear
• Visualisations match variable types
• Exploratory findings are not used as formal hypothesis tests

Reading and reporting the results

Summarise descriptive statistics in a table and present two to four key plots supporting modelling decisions. Explain missing data, outliers, and strong correlations.

Example Python code

A short starting script with this project's variable names. The delivered version is adapted to your data and fully commented.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

rng = np.random.default_rng(35)
n = 250
total_assets = rng.normal(40, 8, n)
equity = rng.gamma(2.0, 3.0, n)
net_profit = 0.5 * total_assets + 2 * equity + rng.normal(0, 5, n)
df = pd.DataFrame({"x1": total_assets, "x2": equity, "y": net_profit})
df.loc[rng.choice(n, 12, replace=False), "x2"] = np.nan

print(df.shape)
print(df.dtypes)
print(df.describe().round(2))
print("Missing per column:\n", df.isna().sum())
corr = df.corr()
print(corr.round(2))

fig, ax = plt.subplots(2, 2, figsize=(10, 8))
ax[0, 0].hist(df["x1"], bins=25); ax[0, 0].set_title("x1")
ax[0, 1].hist(df["x2"].dropna(), bins=25); ax[0, 1].set_title("x2")
ax[1, 0].boxplot([df["x1"], df["x2"].dropna(), df["y"]])
ax[1, 0].set_xticklabels(["x1", "x2", "y"])
im = ax[1, 1].imshow(corr, vmin=-1, vmax=1, cmap="coolwarm")
ax[1, 1].set_xticks(range(3)); ax[1, 1].set_xticklabels(corr.columns)
ax[1, 1].set_yticks(range(3)); ax[1, 1].set_yticklabels(corr.columns)
plt.colorbar(im, ax=ax[1, 1])
plt.tight_layout()
plt.show()

Packages and prices

Choose how much help you need. Each higher package includes everything in the lower ones. Prices exclude 11% VAT.

• Payment: 50% down payment before work starts, balance after the deliverables are handed over
• Tax: Prices exclude 11% VAT
• Data and academic integrity: Results are computed from your own research data; you write and stand behind your manuscript, we help with the analysis and your understanding of it
• Revisions: Up to 2 revisions are included in the price (Package 3: 3 rounds)

Frequently asked questions