Course Content
Day-2: How to use VScode (an IDE) for Python?
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Day-3: Basics of Python Programming
This section will train you for Python programming language
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Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
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Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
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Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
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Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
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Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
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Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
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Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
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Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
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Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
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Day-39: git and github
We will learn about git and github
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Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
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Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

In this lecture you will learn how to improt dataset in pandas and run basic commands.

The Dataset used here in this section is already uploaded here.

 

The Link to Pandas library is here

 

 

 

Here are some of the key reasons why the Pandas library is important in Python:

📊 Data Manipulation: Pandas allows you to easily manipulate large datasets using its powerful DataFrame data structure. You can select, filter, group, pivot and transform your data efficiently.

📈 Data Analysis: Common data analysis tasks like cleaning, wrangling, aggregating, joining and reshaping data are built into Pandas fundamentally. This accelerates your analysis workflow.

📝 Data Visualization: While not as fully-featured as Matplotlib/Seaborn, Pandas makes basic data visualization very easy via plotting methods on DataFrames. Great for quick exploratory plots.

🗓 Time-Series Data: Pandas has excellent support for loading, working with and manipulating datetime data. This makes it very suitable for analysing financial, business or any temporal data.

📦 Data IO: Pandas simplifies data import/export from CSV, Excel, SQL, JSON and other formats using optimized read/write methods. Great for quick ETL tasks.

📡 APIs: Pandas works seamlessly with popular APIs like Google Sheets, SQL, REST APIs to provide more functionality. Expand your data science beyond local datasets.

⚡ Speed: Optimized data structures and algorithms ensure Pandas code runs very fast even on huge datasets compared to raw Python. Ideal for big data tasks.

In summary, Pandas empowers data scientists with an integrated toolkit for data wrangling, munging, cleaning, analysis and visualization in Python – making it an essential library for data manipulation and analysis tasks.

 

 

Exercise Files
pandas_01_day6.xlsx
Size: 9.35 KB
Join the conversation
Amina Rasheed 5 months ago
in contrast, kindly any one here can guide type casting in Pandas.
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Amina Rasheed 5 months ago
am stuck in type casting in Pandas after searching on google about 1 n half hours am move forward hoping that in the next video "baba g ny clear kr dita hona ay"
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Sehar shafi 5 months ago
Info. () is a function And df. Shape is an attribute
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Ghayas uddin 6 months ago
To Import dataset/excel file in python: print(r"path name/file name")
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Akhtar Alvi 8 months ago
#### for reading Excel files in pandas, need to install the 'openpyxl' library as pip install openpyxl
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Farah Kaleem 8 months ago
Alhumdullillah. Markdown language is clear.
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Muhammad Najaf Nisar 9 months ago
df["height"] = df["height"].astype("float")
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Abu Hammad 9 months ago
# Assignment # Write the path if you want to load data from day_1 to day_6pd.read_excel("../day_1/pandas_01_day6.xlsx")
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Muhammad Najaf Nisar 9 months ago
Very beautifully explained about pandas, 100% understanding Aammar baba Allah bless you. keep growing hope so sooner I will join hands with you in this journey.
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TAHA EHSAN ULLAH 9 months ago
Add path of the file before writing the name of the file like this: df = pd.read_csv("d:sales_data_sample.csv")
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