pandas

__HTML_TAG_178_ All pandas Commands _ Generar pandas PDF Guide

Pandas Cheatsheet

Instalación

Platform	Command
pip (All platforms)	INLINE_CODE_8
conda	INLINE_CODE_9
Ubuntu/Debian	INLINE_CODE_10
macOS (Homebrew)	INLINE_CODE_11
With performance libs	INLINE_CODE_12
With all dependencies	INLINE_CODE_13
Excel support	INLINE_CODE_14
SQL support	INLINE_CODE_15
Verify installation	INLINE_CODE_16

Comandos Básicos - Creación de DataFrame I/O

Command	Description
INLINE_CODE_17	Create DataFrame from dictionary
INLINE_CODE_18	Create DataFrame from list of lists
INLINE_CODE_19	Create a Series (single column)
INLINE_CODE_20	Read CSV file into DataFrame
INLINE_CODE_21	Read CSV with custom delimiter and encoding
INLINE_CODE_22	Read Excel file
INLINE_CODE_23	Read JSON file
INLINE_CODE_24	Read from SQL database
INLINE_CODE_25	Read Parquet file
INLINE_CODE_26	Read data from clipboard
INLINE_CODE_27	Write DataFrame to CSV
INLINE_CODE_28	Write DataFrame to Excel
INLINE_CODE_29	Write DataFrame to JSON
INLINE_CODE_30	Write DataFrame to SQL database

Comandos Básicos - Inspección e Información

Command	Description
INLINE_CODE_31	Display first 5 rows
INLINE_CODE_32	Display first 10 rows
INLINE_CODE_33	Display last 5 rows
INLINE_CODE_34	Get dimensions (rows, columns)
INLINE_CODE_35	Get column names
INLINE_CODE_36	Get data types of all columns
INLINE_CODE_37	Get index information
INLINE_CODE_38	Get concise summary of DataFrame
INLINE_CODE_39	Get statistical summary of numeric columns
INLINE_CODE_40	Get statistical summary of all columns
INLINE_CODE_41	Count non-null values per column
INLINE_CODE_42	Count unique values per column
INLINE_CODE_43	Get memory usage of DataFrame
INLINE_CODE_44	Get random sample of 5 rows
INLINE_CODE_45	Get column names as list

Comandos básicos - Selección " Filtro

Command	Description
INLINE_CODE_46	Select single column (returns Series)
INLINE_CODE_47	Select multiple columns (returns DataFrame)
INLINE_CODE_48	Select first row by position
INLINE_CODE_49	Select first 5 rows by position
INLINE_CODE_50	Select all rows, first 2 columns
INLINE_CODE_51	Select row by label/index
INLINE_CODE_52	Select columns by label range
INLINE_CODE_53	Filter rows where column > 5
INLINE_CODE_54	Filter rows by list of values
INLINE_CODE_55	Filter with multiple conditions (AND)
INLINE_CODE_56	Filter with multiple conditions (OR)
INLINE_CODE_57	Filter with NOT condition
INLINE_CODE_58	Filter using SQL-like query string
INLINE_CODE_59	Get 5 rows with largest values in column
INLINE_CODE_60	Get 5 rows with smallest values in column

Comandos básicos - Manipulación de datos

Command	Description
INLINE_CODE_61	Add new column with calculation
INLINE_CODE_62	Delete column
INLINE_CODE_63	Delete rows by index
INLINE_CODE_64	Rename columns
INLINE_CODE_65	Sort by column values descending
INLINE_CODE_66	Sort by multiple columns
INLINE_CODE_67	Sort by index
INLINE_CODE_68	Reset index to default integer index
INLINE_CODE_69	Set column as index
INLINE_CODE_70	Get unique values in column
INLINE_CODE_71	Count occurrences of each value
INLINE_CODE_72	Remove duplicate rows
INLINE_CODE_73	Remove duplicates based on specific columns
INLINE_CODE_74	Fill missing values with 0
INLINE_CODE_75	Remove rows with any missing values

Comandos básicos - Aggregation

Command	Description
INLINE_CODE_76	Sum of column values
INLINE_CODE_77	Mean of column values
INLINE_CODE_78	Median of column values
INLINE_CODE_79	Standard deviation
INLINE_CODE_80	Variance
INLINE_CODE_81	Minimum value
INLINE_CODE_82	Maximum value
INLINE_CODE_83	Get 75th percentile
INLINE_CODE_84	Group by column and sum
INLINE_CODE_85	Group by and calculate mean of specific column
INLINE_CODE_86	Count rows per group
INLINE_CODE_87	Multiple aggregations
INLINE_CODE_88	Correlation matrix of numeric columns
INLINE_CODE_89	Correlation between two columns

Uso avanzado - Selección compleja & Filtro

Command	Description
INLINE_CODE_90	Select columns by data type
INLINE_CODE_91	Exclude columns by data type
INLINE_CODE_92	Filter by string pattern
INLINE_CODE_93	Filter by string prefix
INLINE_CODE_94	Filter by string suffix
INLINE_CODE_95	Filter by regex pattern
INLINE_CODE_96	Filter using callable/lambda
INLINE_CODE_97	Fast scalar value access
INLINE_CODE_98	Fast scalar value access by position
INLINE_CODE_99	Cross-section selection in MultiIndex
INLINE_CODE_100	Filter columns by name pattern
INLINE_CODE_101	Filter columns by regex

Uso avanzado - Transformación de datos

Command	Description
INLINE_CODE_102	Apply function to each element
INLINE_CODE_103	Apply function to each column
INLINE_CODE_104	Apply function element-wise (deprecated, use INLINE_CODE_105)
INLINE_CODE_106	Apply function element-wise to DataFrame
INLINE_CODE_107	Map values using dictionary
INLINE_CODE_108	Replace values
INLINE_CODE_109	Replace infinite values with NaN
INLINE_CODE_110	Change data types
INLINE_CODE_111	Convert to datetime
INLINE_CODE_112	Convert to numeric, invalid → NaN
INLINE_CODE_113	Convert strings to lowercase
INLINE_CODE_114	Remove leading/trailing whitespace
INLINE_CODE_115	Split strings into columns
INLINE_CODE_116	Bin continuous values into intervals
INLINE_CODE_117	Bin values into quantile-based intervals

Advanced Usage - Merging & Joining

Command	Description
INLINE_CODE_118	Inner join on key column
INLINE_CODE_119	Left join
INLINE_CODE_120	Right join
INLINE_CODE_121	Full outer join
INLINE_CODE_122	Join on different column names
INLINE_CODE_123	Join on multiple keys
INLINE_CODE_124	Add merge indicator column
INLINE_CODE_125	Index-based join with suffixes
INLINE_CODE_126	Concatenate vertically
INLINE_CODE_127	Concatenate horizontally
INLINE_CODE_128	Concatenate with hierarchical index
INLINE_CODE_129	Merge on nearest key (time series)

Advanced Usage - Reshaping & Pivoting

__TABLE_193_

Advanced Usage - GroupBy Operations

Command	Description
INLINE_CODE_142	Multiple aggregations
INLINE_CODE_143	Different aggregations per column
INLINE_CODE_144	Named aggregations
INLINE_CODE_145	Transform with group-wise operation
INLINE_CODE_146	Filter groups by condition
INLINE_CODE_147	Get first row of each group
INLINE_CODE_148	Cumulative sum within groups
INLINE_CODE_149	Rank within groups
INLINE_CODE_150	Shift values within groups
INLINE_CODE_151	Expanding window within groups

Advanced Usage - Time Series

Command	Description
INLINE_CODE_152	Set datetime column as index
INLINE_CODE_153	Resample to daily frequency with mean
INLINE_CODE_154	Resample to monthly frequency with sum
INLINE_CODE_155	Resample with multiple aggregations
INLINE_CODE_156	7-day rolling average
INLINE_CODE_157	7-day rolling sum
INLINE_CODE_158	Expanding window mean
INLINE_CODE_159	Exponentially weighted moving average
INLINE_CODE_160	Shift values down by 1 period
INLINE_CODE_161	Calculate difference with previous row
INLINE_CODE_162	Calculate percentage change
INLINE_CODE_163	Generate date range
INLINE_CODE_164	Extract year from datetime
INLINE_CODE_165	Extract month from datetime
INLINE_CODE_166	Extract day of week (0=Monday)

Configuración

Mostrar opciones

# Set maximum rows to display
pd.set_option('display.max_rows', 100)

# Set maximum columns to display
pd.set_option('display.max_columns', 50)

# Set column width
pd.set_option('display.max_colwidth', 100)

# Set float display precision
pd.set_option('display.precision', 2)

# Display all columns
pd.set_option('display.expand_frame_repr', False)

# Show full dataframe without truncation
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)

# Reset all options to defaults
pd.reset_option('all')

# Get current option value
pd.get_option('display.max_rows')

Opciones de rendimiento

# Use copy-on-write mode (pandas 2.0+)
pd.options.mode.copy_on_write = True

# Set computation mode
pd.set_option('compute.use_bottleneck', True)
pd.set_option('compute.use_numexpr', True)

# Set chained assignment warning
pd.set_option('mode.chained_assignment', 'warn')  # or 'raise' or None

I/O Options

# Set default CSV separator
pd.set_option('io.excel.xlsx.writer', 'xlsxwriter')

# Set clipboard encoding
pd.set_option('display.clipboard.encoding', 'utf-8')

Common Use Cases

Use Case 1: Data Cleaning Pipeline

import pandas as pd

# Read data
df = pd.read_csv('raw_data.csv')

# Remove duplicates
df = df.drop_duplicates()

# Handle missing values
df['column1'].fillna(df['column1'].mean(), inplace=True)
df.dropna(subset=['critical_column'], inplace=True)

# Fix data types
df['date'] = pd.to_datetime(df['date'])
df['amount'] = pd.to_numeric(df['amount'], errors='coerce')

# Remove outliers (values beyond 3 standard deviations)
df = df[np.abs(df['value'] - df['value'].mean()) <= (3 * df['value'].std())]

# Standardize text
df['name'] = df['name'].str.strip().str.lower()

# Save cleaned data
df.to_csv('cleaned_data.csv', index=False)

Use Case 2: Exploratory Análisis de datos

import pandas as pd

# Load data
df = pd.read_csv('sales_data.csv')

# Basic statistics
print(df.describe())
print(df.info())

# Check for missing values
print(df.isnull().sum())

# Analyze categorical variables
print(df['category'].value_counts())
print(df['region'].value_counts(normalize=True))

# Correlation analysis
correlation_matrix = df.corr()
print(correlation_matrix)

# Group analysis
sales_by_region = df.groupby('region').agg({
    'sales': ['sum', 'mean', 'count'],
    'profit': 'sum'
})
print(sales_by_region)

# Time-based analysis
df['date'] = pd.to_datetime(df['date'])
monthly_sales = df.set_index('date').resample('M')['sales'].sum()
print(monthly_sales)

Use Case 3: Data Integration from Multiple Sources

import pandas as pd

# Read from different sources
customers = pd.read_csv('customers.csv')
orders = pd.read_excel('orders.xlsx')
products = pd.read_json('products.json')

# Merge datasets
df = pd.merge(orders, customers, on='customer_id', how='left')
df = pd.merge(df, products, on='product_id', how='left')

# Calculate derived metrics
df['total_price'] = df['quantity'] * df['unit_price']
df['profit'] = df['total_price'] - df['cost']

# Aggregate by customer
customer_summary = df.groupby('customer_id').agg({
    'order_id': 'count',
    'total_price': 'sum',
    'profit': 'sum'
}).rename(columns={'order_id': 'order_count'})

# Export results
customer_summary.to_excel('customer_summary.xlsx')

Use Case 4: Time Series Analysis

import pandas as pd

# Read time series data
df = pd.read_csv('stock_prices.csv', parse_dates=['date'])
df.set_index('date', inplace=True)

# Calculate returns
df['daily_return'] = df['close'].pct_change()

# Calculate moving averages
df['MA_7'] = df['close'].rolling(window=7).mean()
df['MA_30'] = df['close'].rolling(window=30).mean()

# Calculate exponential moving average
df['EMA_12'] = df['close'].ewm(span=12).mean()

# Resample to weekly data
weekly_df = df.resample('W').agg({
    'open': 'first',
    'high': 'max',
    'low': 'min',
    'close': 'last',
    'volume': 'sum'
})

# Calculate volatility (rolling standard deviation)
df['volatility'] = df['daily_return'].rolling(window=30).std()

# Export analysis
df.to_csv('stock_analysis.csv')

Use Case 5: Transformación de datos para el aprendizaje automático

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Load data
df = pd.read_csv('ml_dataset.csv')

# Handle missing values
df.fillna(df.median(numeric_only=True), inplace=True)

# Encode categorical variables
df = pd.get_dummies(df, columns=['category', 'region'], drop_first=True)

# Create features
df['feature_ratio'] = df['feature1'] / (df['feature2'] + 1)
df['feature_interaction'] = df['feature1'] * df['feature2']

# Separate features and target
X = df.drop('target', axis=1)
y = df['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Scale features
scaler = StandardScaler()
X_train_scaled = pd.DataFrame(
    scaler.fit_transform(X_train),
    columns=X_train.columns,
    index=X_train.index
)
X_test_scaled = pd.DataFrame(
    scaler.transform(X_test),
    columns=X_test.columns,
    index=X_test.index
)

Buenas prácticas

• Use inplace=False por defecto: Evite inplace=True_ para mantener la inmutabilidad y facilitar la depuración. Asignar resultados a nuevas variables en su lugar.

Vectorizar operaciones Use pandas operaciones vectorizadas incorporadas en lugar de bucles. Las operaciones como df['col'] * 2 son mucho más rápidas que df['col'].apply(lambda x: x * 2).

• ** Operaciones de cambio eficientemente**: Usar encadenamiento de método con el formato adecuado para el código legible: df.query('col > 5').groupby('category').sum()_.

• Especifique dtipos al leer datos: Use dtype_ parámetro in read_csv() para reducir el uso de la memoria y mejorar el rendimiento: pd.read_csv('file.csv', dtype={'col1': 'int32'})_.

• Utilice el tipo de datos categóricos para columnas de baja cardiacidad: Convertir columnas de cadena con pocos valores únicos en categorical: df['category'] = df['category'].astype('category')_ para guardar la memoria.

• Los valores perdidos de forma explícita: Siempre busque y maneje los valores perdidos conscientemente en lugar de dejarlos propagar: df.isnull().sum()_ antes de procesar.

• Use copy() cuando sea necesario: Al crear un subconjunto que modificarás