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Here's a list of important Pandas functions along with brief descriptions:
pd.read_csv() – Reads a CSV file into a DataFrame.
pd.DataFrame() – Creates a DataFrame from various input formats (e.g., lists, dictionaries).
df.head() – Displays the first few rows of the DataFrame.
df.tail() – Displays the last few rows of the DataFrame.
df.info() – Provides a concise summary of the DataFrame (data types, non-null counts).
df.describe() – Provides descriptive statistics for numerical columns.
df.columns – Returns the column labels of the DataFrame.
df.index – Returns the index (row labels) of the DataFrame.
df.shape – Returns the dimensions of the DataFrame (rows, columns).
df.dtypes – Returns the data types of each column.
df.isnull() – Detects missing values (returns Boolean values).
df.fillna() – Fills missing values with a specified value.
df.dropna() – Removes missing values from the DataFrame.
df.drop() – Drops specified labels from rows or columns.
df.duplicated() – Returns Boolean Series denoting duplicate rows.
df.drop_duplicates() – Removes duplicate rows from the DataFrame.
df.sort_values() – Sorts the DataFrame by the values of one or more columns.
df.groupby() – Groups data by one or more columns for aggregation.
df.apply() – Applies a function along an axis of the DataFrame.
df.loc[] – Accesses a group of rows and columns by labels or Boolean arrays.
df.iloc[] – Accesses rows and columns by index position.
df.merge() – Merges two DataFrames on common columns or indices.
df.join() – Joins two DataFrames based on their index.
df.concat() – Concatenates multiple DataFrames along a particular axis.
df.pivot_table() – Creates a pivot table for summarizing data.
df.melt() – Unpivots the DataFrame from wide to long format.
df.rename() – Renames columns or index labels of the DataFrame.
df.set_index() – Sets a column as the index of the DataFrame.
df.reset_index() – Resets the index to a default integer index.
pd.to_datetime() – Converts a column or series to datetime format.
pd.cut() – Bins continuous data into discrete intervals.
df.value_counts() – Returns a Series of counts for unique values in a column.
df.corr() – Computes the pairwise correlation between columns.
df.to_csv() – Writes the DataFrame to a CSV file.
df.plot() – Creates basic plots from DataFrame data using Matplotlib.
These functions cover essential operations in data handling, cleaning, analysis, and visualization using Pandas.
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Anyone looking to learn Pandas?
Here’s your step-by-step guide to mastering data analysis..
🎯 Pandas Checklist for Data Aspirants 🚀
🌱 Getting Started with Pandas
👉 Install Pandas and set up Jupyter Notebook
👉 Understand DataFrames and Series (your new best friends!)
🔍 Load & Explore Data
👉 Import data from files (CSV, Excel, etc.)
👉 Get a quick snapshot of data with head(), info(), and describe()
🧹 Data Cleaning Essentials
👉 Handle missing data with fillna() or dropna()
👉 Remove duplicates and filter data as needed
🔄 Transforming Data
👉 Sort and rank values easily
👉 Use apply() and map() for custom transformations
📊 Summarize with Grouping
👉 Group data by categories with groupby()
👉 Create quick pivot tables for summaries
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👉 Work with MultiIndex for complex data
👉 Reshape data with pivot() and melt()
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👉 Reduce memory usage by adjusting data types
👉 Use vectorized operations for speed
📂 Practice Projects
👉 Apply your skills on real datasets
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02:06
12.02.2025
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Python Important Patterns ✅
1538
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11.02.2025
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