Cheat Sheet: Working with Data in Python

 

Working with Data in Python Cheat Sheet

Reading and writing files

Package/Method	Description	Syntax and Code Example
File opening modes	Different modes to open files for specific operations.	Syntax: r (reading) w (writing) a (appending) + (updating: read/write) b (binary, otherwise text) 1 Examples: with open("data.txt", "r") as file: content = file.read() print(content) with open("output.txt", "w") as file: file.write("Hello, world!") with open("log.txt", "a") as file: file.write("Log entry: Something happened.") with open("data.txt", "r+") as file: content = file.read() file.write("Updated content: " + content)</td>
File reading methods	Different methods to read file content in various ways.	Syntax: 1 2 3 file.readlines() # reads all lines as a list readline() # reads the next line as a string file.read() # reads the entire file content as a string Example: 1 2 3 4 with open("data.txt", "r") as file: lines = file.readlines() next_line = file.readline() content = file.read()
File writing methods	Different write methods to write content to a file.	Syntax: 1 2 file.write(content) # writes a string to the file file.writelines(lines) # writes a list of strings to the file Example: 1 2 3 lines = ["Hello\n", "World\n"] with open("output.txt", "w") as file: file.writelines(lines)
Iterating over lines	Iterates through each line in the file using a `loop`.	Syntax: 1 for line in file: # Code to process each line Example: 1 2 with open("data.txt", "r") as file: for line in file: print(line)
Open() and close()	Opens a file, performs operations, and explicitly closes the file using the close() method.	Syntax: 1 2 file = open(filename, mode) # Code that uses the file file.close() Example: 1 2 3 file = open("data.txt", "r") content = file.read() file.close()
with open()	Opens a file using a with block, ensuring automatic file closure after usage.	Syntax: 1 with open(filename, mode) as file: # Code that uses the file Example: 1 2 with open("data.txt", "r") as file: content = file.read()

Pandas

Package/Method	Description	Syntax and Code Example
.read_csv()	Reads data from a `.CSV` file and creates a DataFrame.	Syntax: dataframe_name = pd.read_csv("filename.csv") Example: df = pd.read_csv("data.csv")
.read_excel()	Reads data from an Excel file and creates a DataFrame.	Syntax: 1 dataframe_name = pd.read_excel("filename.xlsx") Example: 1 df = pd.read_excel("data.xlsx")
.to_csv()	Writes DataFrame to a CSV file.	Syntax: 1 dataframe_name.to_csv("output.csv", index=False) Example: 1 df.to_csv("output.csv", index=False)
Access Columns	Accesses a specific column using [] in the DataFrame.	Syntax: 1 2 dataframe_name["column_name"] # Accesses single column dataframe_name[["column1", "column2"]] # Accesses multiple columns Example: 1 2 df["age"] df[["name", "age"]]
describe()	Generates statistics summary of numeric columns in the DataFrame.	Syntax: 1 dataframe_name.describe() Example: 1 df.describe()
drop()	Removes specified rows or columns from the DataFrame. axis=1 indicates columns. axis=0 indicates rows.	Syntax: 1 2 dataframe_name.drop(["column1", "column2"], axis=1, inplace=True) dataframe_name.drop(index=[row1, row2], axis=0, inplace=True) Example: 1 2 df.drop(["age", "salary"], axis=1, inplace=True) # Will drop columns df.drop(index=[5, 10], axis=0, inplace=True) # Will drop rows
dropna()	Removes rows with missing NaN values from the DataFrame. axis=0 indicates rows.	Syntax: 1 dataframe_name.dropna(axis=0, inplace=True) Example: 1 df.dropna(axis=0, inplace=True)
duplicated()	Duplicate or repetitive values or records within a data set.	Syntax: 1 dataframe_name.duplicated() Example: 1 duplicate_rows = df[df.duplicated()]
Filter Rows	Creates a new DataFrame with rows that meet specified conditions.	Syntax: 1 filtered_df = dataframe_name[(Conditional_statements)] Example: 1 filtered_df = df[(df["age"] > 30) & (df["salary"] < 50000)
groupby()	Splits a DataFrame into groups based on specified criteria, enabling subsequent aggregation, transformation, or analysis within each group.	Syntax: 1 2 grouped = dataframe_name.groupby(by, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, dropna=True) Example: 1 grouped = df.groupby(["category", "region"]).agg({"sales": "sum"})
head()	Displays the first n rows of the DataFrame.	Syntax: 1 dataframe_name.head(n) Example: 1 df.head(5)
Import pandas	Imports the Pandas library with the alias pd.	Syntax: 1 import pandas as pd Example: 1 import pandas as pd
info()	Provides information about the DataFrame, including data types and memory usage.	Syntax: 1 dataframe_name.info() Example: 1 df.info()
merge()	Merges two DataFrames based on multiple common columns.	Syntax: 1 merged_df = pd.merge(df1, df2, on=["column1", "column2"]) Example: 1 merged_df = pd.merge(sales, products, on=["product_id", "category_id"])
print DataFrame	Displays the content of the DataFrame.	Syntax: 1 print(df) # or just type df Example: 1 2 print(df) df
replace()	Replaces specific values in a column with new values.	Syntax: 1 dataframe_name["column_name"].replace(old_value, new_value, inplace=True) Example: 1 df["status"].replace("In Progress", "Active", inplace=True)
tail()	Displays the last n rows of the DataFrame.	Syntax: 1 dataframe_name.tail(n) Example: 1 df.tail(5)

Numpy

Package/Method	Description	Syntax and Code Example
Importing NumPy	Imports the NumPy library.	Syntax: 1 import numpy as np Example: 1 import numpy as np
np.array()	Creates a one or multi-dimensional array,	Syntax: 1 2 array_1d = np.array([list1 values]) # 1D Array array_2d = np.array([[list1 values], [list2 values]]) # 2D Array Example: 1 2 array_1d = np.array([1, 2, 3]) # 1D Array array_2d = np.array([[1, 2], [3, 4]]) # 2D Array
Numpy Array Attributes	- Calculates the mean of array elements - Calculates the sum of array elements - Finds the minimum value in the array - Finds the maximum value in the array - Computes dot product of two arrays	Example: 1 2 3 4 5 np.mean(array) np.sum(array) np.min(array np.max(array) np.dot(array_1, array_2)