The Data

Updated Sep 1, 2026

💡

Read it. Work with it. Save it.

1. Text Files

Python can read and write plain text files.

Use with to open a file safely. It closes automatically when done.

🧑‍💻 Read a File

python

with open("students.txt", "r") as file:
    content = file.read()

print(content)

Read Line by Line

python

with open("students.txt", "r") as file:
    for line in file:
        print(line.strip())

.strip() removes the \n at the end of each line.

Write a File

python

with open("students.txt", "w") as file:
    file.write("Ahmed\n")
    file.write("Faadumo\n")
    file.write("Hodan\n")

"w" overwrites everything in the file.

Append to a File

python

with open("students.txt", "a") as file:
    file.write("Bile\n")

"a" adds to the end without erasing what's already there.

📌 Summary

Task	Code
Read a file	open("file.txt", "r")
Read all text	file.read()
Read lines	for line in file:
Strip newlines	line.strip()
Append to a file	open("file.txt", "a")
Overwrite a file	open("file.txt", "w")
Safe open	with open(...) as file:

2. CSV

CSV stands for Comma-Separated Values.

Each line is a row. Commas separate the columns.

plain text

name,course,score
Ahmed,Python,88
Faadumo,React,92
Hodan,Python,75

Python has a built-in csv module. No install needed.

🧑‍💻 Read a CSV

python

import csv

with open("students.csv", "r") as file:
    reader = csv.reader(file)

    for row in reader:
        print(row)

Each row is a list: ["Ahmed", "Python", "88"]

Read as Dictionaries

python

import csv

with open("students.csv", "r") as file:
    reader = csv.DictReader(file)

    for row in reader:
        print(row["name"], row["score"])

DictReader uses the first row as keys. Each row becomes a dictionary.

Write a CSV

python

import csv

with open("roster.csv", "w", newline="") as file:
    writer = csv.writer(file)

    writer.writerow(["name", "score"])
    writer.writerow(["Ahmed", 88])
    writer.writerow(["Faadumo", 92])

💡 Always pass newline="" when writing CSV. Without it you get blank lines on Windows.

📌 Summary

Task	Code
Import	import csv
Read rows	csv.reader(file)
Read as dicts	csv.DictReader(file)
Write rows	csv.writer(file)
Write one row	writer.writerow([values])
Avoid blank lines	newline=""

3. JSON

JSON stores data as keys and values. It looks like a Python dictionary.

json

{
    "name": "Ahmed",
    "course": "Python",
    "score": 88
}

A JSON file can also hold a list of items:

json

[
    {"name": "Ahmed", "score": 88},
    {"name": "Faadumo", "score": 92},
    {"name": "Hodan", "score": 75}
]

🧑‍💻 Read JSON

python

import json

with open("students.json", "r") as file:
    data = json.load(file)

print(data)

json.load() turns the file into a Python dictionary or list.

Write JSON

python

import json

students = [
    {"name": "Ahmed", "score": 88},
    {"name": "Faadumo", "score": 92}
]

with open("roster.json", "w") as file:
    json.dump(students, file, indent=2)

indent=2 makes the file readable. Without it everything is on one line.

📌 Summary

Task	Code
Import	import json
Read JSON	json.load(file)
Write JSON	json.dump(data, file)
Pretty print	indent=2
Result type	Dictionary or list

4. Pandas

Pandas turns your data into a table you can work with in code.

That table is called a DataFrame.

🧑‍💻 Install

bash

uv add pandas

Read a CSV

python

import pandas as pd

df = pd.read_csv("students.csv")
print(df)

plain text

     name  course  score
0   Ahmed  Python     88
1  Faadumo  React     92
2    Hodan  Python     75

Read JSON

python

df = pd.read_json("students.json")
print(df)

Same result. Different source file, same DataFrame.

Inspect the Data

python

df.head()       # First 5 rows
df.info()       # Column names, types, missing values
df.describe()   # Count, mean, min, max for numbers

Series

A single column is called a Series.

python

names = df["name"]
print(names)

plain text

0      Ahmed
1    Faadumo
2      Hodan
Name: name, dtype: object

💡 DataFrame = the whole table. Series = one column.

📌 Summary

Task	Code
Install	uv add pandas
Import	import pandas as pd
Read CSV	pd.read_csv("file.csv")
Read JSON	pd.read_json("file.json")
First 5 rows	df.head()
Column info	df.info()
Stats	df.describe()
One column	df["column"]

5. Filter, Sort, Save

Select Columns

python

df[["name", "score"]]

Double brackets for multiple columns.

Filter Rows

python

df[df["course"] == "Python"]

Returns only rows where course is "Python".

Multiple Conditions

python

df[(df["course"] == "Python") & (df["score"] > 80)]

& means AND. Each condition needs its own parentheses.

Loc

loc selects by name. You tell it what you want using column names and conditions.

"Give me the name of everyone who scored above 80."

python

df.loc[df["score"] > 80, "name"]

"Give me the name and course of everyone who scored above 80."

python

df.loc[df["score"] > 80, ["name", "course"]]

💡 loc stands for location. You locate data by name.

Sort

python

df.sort_values("score")                    # Low to high
df.sort_values("score", ascending=False)   # High to low

Save to CSV

python

df.to_csv("filtered.csv", index=False)

Save to JSON

python

df.to_json("filtered.json", orient="records", indent=2)

index=False removes the row numbers from the output.

orient="records" saves as a list of objects (the clean format).

📌 Summary

Task	Code
Select columns	df[["col1", "col2"]]
Filter rows	df[df["col"] == "value"]
AND condition	(cond1) & (cond2)
Select by label	df.loc[condition, "col"]
Sort ascending	df.sort_values("col")
Sort descending	ascending=False
Save CSV	df.to_csv("file.csv", index=False)
Save JSON	df.to_json("file.json", orient="records")

6. File Uploader

Let the user upload a file directly in Streamlit.

🧑‍💻 Upload a CSV

python

import streamlit as st
import pandas as pd

uploaded = st.file_uploader("Upload a CSV", type="csv")

if uploaded:
    df = pd.read_csv(uploaded)
    st.dataframe(df)

st.file_uploader returns the file. Pass it straight to Pandas.

st.dataframe displays the table with sorting and scrolling built in.

Upload JSON

python

uploaded = st.file_uploader("Upload JSON", type="json")

if uploaded:
    df = pd.read_json(uploaded)
    st.dataframe(df)

Accept Multiple Types

python

uploaded = st.file_uploader("Upload a file", type=["csv", "json"])

if uploaded:
    if uploaded.name.endswith(".csv"):
        df = pd.read_csv(uploaded)
    else:
        df = pd.read_json(uploaded)

    st.dataframe(df)

💡 Always check if uploaded: before reading. The file is None until the user picks one.

📌 Summary

Task	Code
Upload widget	st.file_uploader("label", type="csv")
Multiple types	type=["csv", "json"]
Read uploaded CSV	pd.read_csv(uploaded)
Read uploaded JSON	pd.read_json(uploaded)
Display table	st.dataframe(df)
Check if uploaded	if uploaded:

The Data

Updated Sep 1, 2026

💡

Read it. Work with it. Save it.

1. Text Files

Python can read and write plain text files.

Use with to open a file safely. It closes automatically when done.

🧑‍💻 Read a File

python

with open("students.txt", "r") as file:
    content = file.read()

print(content)

Read Line by Line

python

with open("students.txt", "r") as file:
    for line in file:
        print(line.strip())

.strip() removes the \n at the end of each line.

Write a File

python

with open("students.txt", "w") as file:
    file.write("Ahmed\n")
    file.write("Faadumo\n")
    file.write("Hodan\n")

"w" overwrites everything in the file.

Append to a File

python

with open("students.txt", "a") as file:
    file.write("Bile\n")

"a" adds to the end without erasing what's already there.

📌 Summary

Task	Code
Read a file	open("file.txt", "r")
Read all text	file.read()
Read lines	for line in file:
Strip newlines	line.strip()
Append to a file	open("file.txt", "a")
Overwrite a file	open("file.txt", "w")
Safe open	with open(...) as file:

2. CSV

CSV stands for Comma-Separated Values.

Each line is a row. Commas separate the columns.

plain text

name,course,score
Ahmed,Python,88
Faadumo,React,92
Hodan,Python,75

Python has a built-in csv module. No install needed.

🧑‍💻 Read a CSV

python

import csv

with open("students.csv", "r") as file:
    reader = csv.reader(file)

    for row in reader:
        print(row)

Each row is a list: ["Ahmed", "Python", "88"]

Read as Dictionaries

python

import csv

with open("students.csv", "r") as file:
    reader = csv.DictReader(file)

    for row in reader:
        print(row["name"], row["score"])

DictReader uses the first row as keys. Each row becomes a dictionary.

Write a CSV

python

import csv

with open("roster.csv", "w", newline="") as file:
    writer = csv.writer(file)

    writer.writerow(["name", "score"])
    writer.writerow(["Ahmed", 88])
    writer.writerow(["Faadumo", 92])

💡 Always pass newline="" when writing CSV. Without it you get blank lines on Windows.

📌 Summary

Task	Code
Import	import csv
Read rows	csv.reader(file)
Read as dicts	csv.DictReader(file)
Write rows	csv.writer(file)
Write one row	writer.writerow([values])
Avoid blank lines	newline=""

3. JSON

JSON stores data as keys and values. It looks like a Python dictionary.

json

{
    "name": "Ahmed",
    "course": "Python",
    "score": 88
}

A JSON file can also hold a list of items:

json

[
    {"name": "Ahmed", "score": 88},
    {"name": "Faadumo", "score": 92},
    {"name": "Hodan", "score": 75}
]

🧑‍💻 Read JSON

python

import json

with open("students.json", "r") as file:
    data = json.load(file)

print(data)

json.load() turns the file into a Python dictionary or list.

Write JSON

python

import json

students = [
    {"name": "Ahmed", "score": 88},
    {"name": "Faadumo", "score": 92}
]

with open("roster.json", "w") as file:
    json.dump(students, file, indent=2)

indent=2 makes the file readable. Without it everything is on one line.

📌 Summary

Task	Code
Import	import json
Read JSON	json.load(file)
Write JSON	json.dump(data, file)
Pretty print	indent=2
Result type	Dictionary or list

4. Pandas

Pandas turns your data into a table you can work with in code.

That table is called a DataFrame.

🧑‍💻 Install

bash

uv add pandas

Read a CSV

python

import pandas as pd

df = pd.read_csv("students.csv")
print(df)

plain text

     name  course  score
0   Ahmed  Python     88
1  Faadumo  React     92
2    Hodan  Python     75

Read JSON

python

df = pd.read_json("students.json")
print(df)

Same result. Different source file, same DataFrame.

Inspect the Data

python

df.head()       # First 5 rows
df.info()       # Column names, types, missing values
df.describe()   # Count, mean, min, max for numbers

Series

A single column is called a Series.

python

names = df["name"]
print(names)

plain text

0      Ahmed
1    Faadumo
2      Hodan
Name: name, dtype: object

💡 DataFrame = the whole table. Series = one column.

📌 Summary

Task	Code
Install	uv add pandas
Import	import pandas as pd
Read CSV	pd.read_csv("file.csv")
Read JSON	pd.read_json("file.json")
First 5 rows	df.head()
Column info	df.info()
Stats	df.describe()
One column	df["column"]

5. Filter, Sort, Save

Select Columns

python

df[["name", "score"]]

Double brackets for multiple columns.

Filter Rows

python

df[df["course"] == "Python"]

Returns only rows where course is "Python".

Multiple Conditions

python

df[(df["course"] == "Python") & (df["score"] > 80)]

& means AND. Each condition needs its own parentheses.

Loc

loc selects by name. You tell it what you want using column names and conditions.

"Give me the name of everyone who scored above 80."

python

df.loc[df["score"] > 80, "name"]

"Give me the name and course of everyone who scored above 80."

python

df.loc[df["score"] > 80, ["name", "course"]]

💡 loc stands for location. You locate data by name.

Sort

python

df.sort_values("score")                    # Low to high
df.sort_values("score", ascending=False)   # High to low

Save to CSV

python

df.to_csv("filtered.csv", index=False)

Save to JSON

python

df.to_json("filtered.json", orient="records", indent=2)

index=False removes the row numbers from the output.

orient="records" saves as a list of objects (the clean format).

📌 Summary

Task	Code
Select columns	df[["col1", "col2"]]
Filter rows	df[df["col"] == "value"]
AND condition	(cond1) & (cond2)
Select by label	df.loc[condition, "col"]
Sort ascending	df.sort_values("col")
Sort descending	ascending=False
Save CSV	df.to_csv("file.csv", index=False)
Save JSON	df.to_json("file.json", orient="records")

6. File Uploader

Let the user upload a file directly in Streamlit.

🧑‍💻 Upload a CSV

python

import streamlit as st
import pandas as pd

uploaded = st.file_uploader("Upload a CSV", type="csv")

if uploaded:
    df = pd.read_csv(uploaded)
    st.dataframe(df)

st.file_uploader returns the file. Pass it straight to Pandas.

st.dataframe displays the table with sorting and scrolling built in.

Upload JSON

python

uploaded = st.file_uploader("Upload JSON", type="json")

if uploaded:
    df = pd.read_json(uploaded)
    st.dataframe(df)

Accept Multiple Types

python

uploaded = st.file_uploader("Upload a file", type=["csv", "json"])

if uploaded:
    if uploaded.name.endswith(".csv"):
        df = pd.read_csv(uploaded)
    else:
        df = pd.read_json(uploaded)

    st.dataframe(df)

💡 Always check if uploaded: before reading. The file is None until the user picks one.

📌 Summary

Task	Code
Upload widget	st.file_uploader("label", type="csv")
Multiple types	type=["csv", "json"]
Read uploaded CSV	pd.read_csv(uploaded)
Read uploaded JSON	pd.read_json(uploaded)
Display table	st.dataframe(df)
Check if uploaded	if uploaded: