Machine Learning

PANDAS — CHEAT SHEET 1. DATA LOADING

Method          | What it does       
----------------+--------------------
pd.read_csv()   | Reads CSV file     
pd.read_excel() | Reads Excel file   
pd.read_sql()   | Reads data from SQL
pd.read_json()  | Reads JSON file    

2. DATA ANALYSIS

Method        | What it does              
--------------+---------------------------
df.head()     | Shows first rows          
df.info()     | Table information         
df.describe() | Statistics by columns     
df.shape      | Table size (rows, columns)
df.columns    | List of column names      

3. DATA SELECTION

Method     | What it does                     
-----------+----------------------------------
df.loc[]   | Selection by row and column names
df.iloc[]  | Selection by indices             
df.query() | Filtering by condition           

4. DATA CLEANING

Method               | What it does                   
---------------------+--------------------------------
df.isnull()          | Check for missing values (NULL)
df.dropna()          | Remove rows with missing values
df.fillna()          | Fill missing values            
df.drop_duplicates() | Remove duplicates              
df.astype()          | Change data type               

5. ANALYTICS

Method            | What it does               
------------------+----------------------------
df.groupby()      | Data grouping              
df.agg()          | Aggregation in groups      
df.value_counts() | Count of unique values     
df.mean()         | Mean value                 
df.median()       | Median                     
df.corr()         | Correlation between columns

6. DATA MERGING

Method      | What it does        
------------+---------------------
pd.merge()  | SQL JOIN by column  
pd.join()   | JOIN by index       
pd.concat() | Glue tables together

⭐ TOP 10 METHODS read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()

PANDAS — CHEAT SHEET 1. DATA LOADING Method          | What it does       ----------------+-------------------- pd.read_csv()   | Reads CSV file     pd.read_excel() | Reads Excel file   pd.read_sql()   | Reads data from SQL pd.read_json()  | Reads JSON file    2. DATA ANALYSIS Method        | What it does              --------------+--------------------------- df.head()     | Shows first rows          df.info()     | Table information         df.describe() | Statistics by columns     df.shape      | Table size (rows, columns) df.columns    | List of column names      3. DATA SELECTION Method     | What it does                     -----------+---------------------------------- df.loc[]   | Selection by row and column names df.iloc[]  | Selection by indices             df.query() | Filtering by condition           4. DATA CLEANING Method               | What it does                   ---------------------+-------------------------------- df.isnull()          | Check for missing values (NULL) df.dropna()          | Remove rows with missing values df.fillna()          | Fill missing values            df.drop_duplicates() | Remove duplicates              df.astype()          | Change data type               5. ANALYTICS Method            | What it does               ------------------+---------------------------- df.groupby()      | Data grouping              df.agg()          | Aggregation in groups      df.value_counts() | Count of unique values     df.mean()         | Mean value                 df.median()       | Median                     df.corr()         | Correlation between columns 6. DATA MERGING Method      | What it does        ------------+--------------------- pd.merge()  | SQL JOIN by column  pd.join()   | JOIN by index       pd.concat() | Glue tables together ⭐ TOP 10 METHODS read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()

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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼 The chain looks neat, but breaks on real data and easily gives unexpected results:

s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]

The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯 It's more reliable to gather everything into one expression:

s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]

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