15.3 Pandas

import pandas as pd

# Create a dictionary of columns
data = {
  "Name": ["Emma", "Gireeja", "Sophia"],
  "Age": [15, 28, 22],
  "City": ["Dubai", "London", "San Jose"]
}

# Create a DataFrame from the dictionary
df = pd.DataFrame(data)

# Display the DataFrame
df

The pd.DataFrame() function takes in a dictionary and converts it into a DataFrame. Dictionary keys will be column labels and values are stored in respective columns.

import pandas as pd

# Create a list of rows
data = [
  ["Emma", 15, "Dubai"],
  ["Gireeja", 28, "London"],
  ["Sophia", 22, "San Jose"]
]

# Define column labels
columns = ["Name", "Age", "City"]

# Create a DataFrame from list using column labels
df = pd.DataFrame(data, columns=columns)

# Display the DataFrame
df

The pd.DataFrame() function takes in a list containing the records in different rows of a DataFrame, along with a list of column labels, and creates a DataFrame with the given rows and column labels.

import numpy as np
import pandas as pd

# Create a NumPy array
data = np.array([
  [1, 0, 0],
  [0, 1, 0],
  [2, 3, 4]
])

# Define column labels
columns = ["A", "B", "C"]

# Create a DataFrame from the NumPy array
df = pd.DataFrame(data, columns=columns)

# Display the DataFrame
df

A NumPy array, along with column labels, are passed to the pd.DataFrame() function to create a DataFrame object.

import pandas as pd

# Read the CSV file into a DataFrame
df = pd.read_csv("data.csv")

# Display the DataFrame
df

The pd.read_csv() function reads a CSV file into a DataFrame and organizes the content in a tabular format.

import pandas as pd

# Read the Excel file into a DataFrame
df = pd.read_excel("data.xlsx")

# Display the DataFrame
df

The pd.read_excel() function reads an Excel file into a DataFrame and organizes the content in a tabular format.

head(n)

Returns the first n rows. If a value is not passed, the first 5 rows will be shown.

df.head(4)

tail(n)

Returns the last n rows. If a value is not passed, the last 5 rows will be shown.

df.tail(3)

info()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 4 columns):
 #    Column        Non-Null Count    Dtype
---   ------        --------------    -----
 0    Season        6 non-null        object
 1    Month         6 non-null        object
 2    Month-day     6 non-null        int64
 3    Year          6 non-null        int64
dtypes: int64(2), object(2)
memory usage: 320.0+ bytes

describe()

df.describe()

value_counts()

df.value_counts \('Season')

Season
Fall  2
Summer  2
Winter  2
dtype: int64

unique()

df['Season'] \.unique()

​​['Summer' 'Fall' 'Winter']