Python #Pandas Cheat Sheet 🐼

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Reverse a list in Python

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Must practise the following questions for your next Python interview:

1. How would you handle missing values in a dataset?

2. Write a python code to merge datasets based on a common column.

3. How would you analyse the distribution of a continuous variable in dataset?

4. Write a python code to pivot an dataframe.

5. How would you handle categorical variables with many levels?

6. Write a python code to calculate the accuracy, precision, and recall of a classification model?

7. How would you handle errors when working with large datasets?

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Practice projects to consider:

1. Implement a basic search engine: Read a set of documents and build an index of keywords. Then, implement a search function that returns a list of documents that match the query.

2. Build a recommendation system: Read a set of user-item interactions and build a recommendation system that suggests items to users based on their past behavior.

3. Create a data analysis tool: Read a large dataset and implement a tool that performs various analyses, such as calculating summary statistics, visualizing distributions, and identifying patterns and correlations.

4. Implement a graph algorithm: Study a graph algorithm such as Dijkstra's shortest path algorithm, and implement it in Python. Then, test it on real-world graphs to see how it performs.

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Data Analyst INTERVIEW QUESTIONS AND ANSWERS
👇👇

1.Can you name the wildcards in Excel?

Ans: There are 3 wildcards in Excel that can ve used in formulas.

Asterisk (*) – 0 or more characters. For example, Ex* could mean Excel, Extra, Expertise, etc.

Question mark (?) – Represents any 1 character. For example, R?ain may mean Rain or Ruin.

Tilde (~) – Used to identify a wildcard character (~, *, ?). For example, If you need to find the exact phrase India* in a list. If you use India* as the search string, you may get any word with India at the beginning followed by different characters (such as Indian, Indiana). If you have to look for India” exclusively, use ~.

Hence, the search string will be india~*. ~ is used to ensure that the spreadsheet reads the following character as is, and not as a wildcard.


2.What is cascading filter in tableau?

Ans: Cascading filters can also be understood as giving preference to a particular filter and then applying other filters on previously filtered data source. Right-click on the filter you want to use as a main filter and make sure it is set as all values in dashboard then select the subsequent filter and select only relevant values to cascade the filters. This will improve the performance of the dashboard as you have decreased the time wasted in running all the filters over complete data source.


3.What is the difference between .twb and .twbx extension?

Ans:
A .twb file contains information on all the sheets, dashboards and stories, but it won’t contain any information regarding data source. Whereas .twbx file contains all the sheets, dashboards, stories and also compressed data sources. For saving a .twbx extract needs to be performed on the data source. If we forward .twb file to someone else than they will be able to see the worksheets and dashboards but won’t be able to look into the dataset.


4.What are the various Power BI versions?

Power BI Premium capacity-based license, for example, allows users with a free license to act on content in workspaces with Premium capacity. A user with a free license can only use the Power BI service to connect to data and produce reports and dashboards in My Workspace outside of Premium capacity. They are unable to exchange material or publish it in other workspaces. To process material, a Power BI license with a free or Pro per-user license only uses a shared and restricted capacity. Users with a Power BI Pro license can only work with other Power BI Pro users if the material is stored in that shared capacity. They may consume user-generated information, post material to app workspaces, share dashboards, and subscribe to dashboards and reports. Pro users can share material with users who don’t have a Power BI Pro subscription while workspaces are at Premium capacity.

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Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:

• Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.

• Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.

• Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.

But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.

No matter where your path leads, the key is to start now.

𝗜𝗺𝗽𝗿𝗲𝘀𝘀 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝟱 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀!😍

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Pandas is a popular Python library for data manipulation and analysis. Here are some essential concepts in Pandas that every data analyst should be familiar with:

1. Data Structures: Pandas provides two main data structures: Series and DataFrame. A Series is a one-dimensional array-like object, while a DataFrame is a two-dimensional tabular data structure similar to a spreadsheet.

2. Indexing and Selection: Pandas allows you to select and manipulate data using various indexing techniques, such as label-based indexing (loc), integer-based indexing (iloc), and boolean indexing.

3. Data Cleaning: Pandas provides functions for handling missing data, removing duplicates, and filling in missing values. Methods like dropna(), fillna(), and drop_duplicates() are commonly used for data cleaning.

4. Data Manipulation: Pandas offers powerful tools for data manipulation, such as merging, joining, concatenating, reshaping, and grouping data. Functions like merge(), concat(), pivot_table(), and groupby() are commonly used for data manipulation tasks.

5. Data Aggregation: Pandas allows you to aggregate data using functions like sum(), mean(), count(), min(), max(), and custom aggregation functions. These functions help summarize and analyze data at different levels.

6. Time Series Analysis: Pandas has built-in support for working with time series data, including date/time indexing, resampling, shifting, rolling window calculations, and time zone handling.

7. Data Visualization: Pandas integrates well with popular data visualization libraries like Matplotlib and Seaborn to create visualizations directly from DataFrames. You can plot data using functions like plot(), hist(), scatter(), and boxplot().

8. Handling Categorical Data: Pandas provides support for working with categorical data through the Categorical data type. This helps in efficient storage and analysis of categorical variables.

9. Reading and Writing Data: Pandas can read data from various file formats such as CSV, Excel, SQL databases, JSON, and HTML. It can also write data back to these formats after processing.

10. Performance Optimization: Pandas offers methods to optimize performance, such as vectorized operations (using NumPy arrays), using apply() function efficiently, and avoiding loops for faster data processing.

By mastering these essential concepts in Pandas, you can efficiently manipulate and analyze data, perform complex operations, and derive valuable insights from your datasets as a data analyst. Regular practice and hands-on experience with Pandas will further enhance your skills in data manipulation and analysis.

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