Conceptos basicos de Pandas

import pandas as pd
import numpy as np

# From a dictionary
data = {
    "name": ["Alice", "Bob", "Charlie", "Diana", "Eve"],
    "age": [30, 25, 35, 28, 32],
    "city": ["NYC", "London", "Tokyo", "Paris", "Berlin"],
    "salary": [95000, 72000, 110000, 88000, 105000],
}
df = pd.DataFrame(data)
print(df)

# From a CSV file
# df = pd.read_csv("data.csv")

# From JSON
# df = pd.read_json("data.json")

# From SQL
# import sqlite3
# conn = sqlite3.connect("database.db")
# df = pd.read_sql("SELECT * FROM users", conn)

# Basic info
print(df.shape)      # (5, 4)
print(df.dtypes)     # Column data types
print(df.describe()) # Statistical summary
print(df.info())     # Memory usage and non-null counts
print(df.head(3))    # First 3 rows
print(df.tail(2))    # Last 2 rows

import pandas as pd

df = pd.DataFrame({
    "name": ["Alice", "Bob", "Charlie", "Diana", "Eve"],
    "age": [30, 25, 35, 28, 32],
    "department": ["Engineering", "Marketing", "Engineering", "Marketing", "Engineering"],
    "salary": [95000, 72000, 110000, 88000, 105000],
})

# Select columns
print(df["name"])            # Series
print(df[["name", "age"]])   # DataFrame with two columns

# Select rows by index
print(df.iloc[0])       # First row
print(df.iloc[0:3])     # First three rows
print(df.iloc[0, 1])    # First row, second column

# Select rows by label
df.index = ["a", "b", "c", "d", "e"]
print(df.loc["a"])           # Row with label 'a'
print(df.loc["a":"c"])       # Rows a through c (inclusive!)

# Boolean filtering
young = df[df["age"] < 30]
engineers = df[df["department"] == "Engineering"]
high_earners = df[df["salary"] > 100000]

# Multiple conditions
senior_engineers = df[
    (df["department"] == "Engineering") & (df["age"] > 30)
]

# isin for multiple values
selected = df[df["name"].isin(["Alice", "Bob", "Eve"])]

# query method (more readable)
result = df.query("department == 'Engineering' and salary > 100000")

# String methods
df[df["name"].str.startswith("A")]
df[df["name"].str.contains("li")]

# Adding columns
df["bonus"] = df["salary"] * 0.1
df["senior"] = df["age"] >= 30

# Apply function to a column
df["name_upper"] = df["name"].apply(str.upper)
df["tax"] = df["salary"].apply(lambda s: s * 0.3 if s > 90000 else s * 0.2)

# Renaming columns
df = df.rename(columns={"name": "full_name", "age": "years"})

# Sorting
df_sorted = df.sort_values("salary", ascending=False)
df_sorted = df.sort_values(["department", "salary"], ascending=[True, False])

# Group by and aggregate
dept_stats = df.groupby("department").agg(
    avg_salary=("salary", "mean"),
    max_salary=("salary", "max"),
    count=("name", "count"),
    avg_age=("age", "mean"),
)
print(dept_stats)

# Pivot tables
pivot = df.pivot_table(
    values="salary",
    index="department",
    columns="senior",
    aggfunc="mean"
)

# Value counts
print(df["department"].value_counts())

import pandas as pd
import numpy as np

df = pd.DataFrame({
    "A": [1, 2, np.nan, 4],
    "B": [np.nan, 2, 3, 4],
    "C": [1, 2, 3, np.nan],
})

# Detect missing values
print(df.isna())         # Boolean mask
print(df.isna().sum())   # Count per column

# Drop missing values
df.dropna()              # Drop rows with any NaN
df.dropna(subset=["A"])  # Drop only if A is NaN
df.dropna(how="all")     # Drop only if all values are NaN

# Fill missing values
df.fillna(0)                    # Fill with constant
df["A"].fillna(df["A"].mean())  # Fill with mean
df.fillna(method="ffill")       # Forward fill
df.fillna(method="bfill")       # Backward fill

# Interpolation
df["A"].interpolate()           # Linear interpolation

import pandas as pd

orders = pd.DataFrame({
    "order_id": [1, 2, 3, 4],
    "customer_id": [101, 102, 101, 103],
    "amount": [250, 180, 320, 150],
})

customers = pd.DataFrame({
    "customer_id": [101, 102, 103, 104],
    "name": ["Alice", "Bob", "Charlie", "Diana"],
    "city": ["NYC", "London", "Tokyo", "Paris"],
})

# Merge (SQL-style join)
merged = pd.merge(orders, customers, on="customer_id", how="inner")
print(merged)

# Left join (keep all orders)
left = pd.merge(orders, customers, on="customer_id", how="left")

# Concatenate DataFrames
df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
df2 = pd.DataFrame({"A": [5, 6], "B": [7, 8]})
combined = pd.concat([df1, df2], ignore_index=True)

# Export
# df.to_csv("output.csv", index=False)
# df.to_json("output.json", orient="records")
# df.to_excel("output.xlsx", index=False)