Analyse Sales Data like a Pro with Python | #dataanalysis #python #datascience

📩 Download All 15 Projects Source Codes + Datasets : https://rzp.io/rzp/all15projects Download Dataset - https://docs.google.com/spreadsheets/d/1avHJCkFv12UX7OlERl41k7p21v4Wu-Ls/edit?usp=sharing&ouid=101357720424425453599&rtpof=true&sd=true 👉Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal 👉Purchase - Python Data Analysis Self Study Notes & All Projects Source Codes (Rs.499 only) - https://rzp.io/l/dslnotes239 👉Download Project (Python Code & Dataset) - https://rzp.io/rzp/project9 ------------- Watch our videos in Hindi on new channel: https://www.youtube.com/@ITCoursesEasy ------------- 👉Enroll in our Udemy courses : 1. Python Data Analytics 13 Projects - https://www.udemy.com/course/bigdata-analysis-python/?referralCode=F75B5F25D61BD4E5F161 2. Python For Data Science - https://www.udemy.com/course/python-for-data-science-real-time-exercises/?referralCode=9C91F0B8A3F0EB67FE67 3. Numpy For Data Science - https://www.udemy.com/course/python-numpy-exercises/?referralCode=FF9EDB87794FED46CBDF ------------- 👉Join Facebook Group - https://www.facebook.com/groups/datasciencelovers 👉Join WhatsApp Group - https://chat.whatsapp.com/JKBkj7Lc9Ba6ZpzVSP5N4O 👉Join LinkedIn Group - https://www.linkedin.com/groups/9247278 👉Follow on Instagram - https://www.instagram.com/data_science_lovers Contact Mail Id : [email protected] ------------- In this video, you will learn how to do Sales Data Analysis with Python. First the data is cleaned and modified, then the questions are given in the project that are solved with the help of Python. It is a project of Data Analysis with Python or you can say, Data Science with Python. The commands that we used in this video : * reset_index() - To convert the index of a Series into a column to form a DataFrame. * loc[ ] - To show any row's values. * info() - To provide the basic information about the dataframe. * drop() - To drop any column or row from the dataframe. * str.strip().str.replace(r'\s+', ' ', regex=True) - To remove extra spaces in any text column. * duplicated() - To show all the duplicate records from a dataframe. * drop_duplicates(inplace=True) - To remove the duplicate records from the dataframe. * round() - To round-off the values of a numerical column. * pd.to_datetime() - To convert the datatype of date column into datetime format. * groupby() - To make the group of all unique values of a column. * std() - To check the standard deviation of any numerical column. * var() - To check the variance of any numerical column. * mean() - To check the mean of any numerical column. * agg() - Using agg() with groupby(). * head() - It shows the first N rows in the data (by default, N=5). * columns - To show all the column names of the dataframe. * unique() - In a column, it shows all the unique values. It can be applied on a single column only, not on the whole dataframe. * nunique() - It shows the total no. of unique values in each column. It can be applied on a single column as well as on the whole dataframe. * describe() - To show some summary about the columns. * astype() - To change the datatype of any column. * dtype - To check the datatype of any column. * value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only. * plot(kind='bar') - To draw the bar graph. * type() - To the type of any variable. * plt.figure(figsize = ()) - To set the size of any figure. * plt.title(), plt.xlabel(), plt.ylabel() - To set the Title, x-axis label, y-axis label. * sort_values(ascending = False) - To sort the values in descending order. * dt.month - To create a new column showing Month only. --------------- Q.1) What was the Most Preferred Payment Method ? Q.2) Which one was the Most Selling Product - By Quantity - By Revenue Q.3) Which City had maximum revenue, or, Which Manager earned maximum revenue ? Q.4) Show the Date wise revenue. Q.5) What was the Average Revenue ? Q.6) What was the Average Revenue of November & December month ? Q.7) What was the Standard Deviation of Revenue and Quantity ? Q.8) What was the Variance of Revenue and Quantity ? Q.9) Was the revenue increasing or decreasing over the time? Q.10) What was the Average 'Quantity Sold' & 'Average Revenue' for each product ? ---------------- You must check our other Data Analytics Projects : Project 13 - HR Data Analytics - https://youtu.be/fykrwQD3HR4 Project 12 - AI Market Financial Data Analysis - https://youtu.be/WmJYHz_qn5s Project 11 - Airlines' Flights Data Analysis - https://youtu.be/gu3Ot78j_Gc Project 10 - Spotify-YouTube Data Analysis - https://youtu.be/xqtbBosGMl0 Project 8 - https://youtu.be/b7Kd0fLwgO4 Project 7 - https://youtu.be/AO5uhxa1R84 Project 6 - https://youtu.be/e1zKFSrKeLs Project 5 - https://youtu.be/q-Omt6LgRLc Project 4 - https://youtu.be/89eYAAPyRfo Project 3 - https://youtu.be/GyUbo45mVSE Project 2 - https://www.youtube.com/watch?v=fhiUl7f5DnI Project 1 - https://youtu.be/4hYOkHijtNw