Pandas cheat sheet: Top 35 commands and operations

Pandas import convention

import pandas as pd

Pandas is now accessible with the acronym pd. You can also install Pandas using the built-in Python tool pip and run the following command.

$ pip install pandas

series1 = pd.Series([1,2,3,4]), index=['a', 'b', 'c', 'd'])

srs.name = "Insert name"

srs.index.name = "Index name"

df = pd.DataFrame(
         {"a" : [1 ,2, 3],
          "b" : [7, 8, 9],
          "c" : [10, 11, 12]},        index = [1, 2, 3])

Specify values in DataFrame columns

Specify how you want to organize your DataFrame by columns.

df = pd.DataFrame( 
     [[1, 2, 3], 
     [4, 6, 8],
     [10, 11, 12]],
     index=[1, 2, 3], 
     columns=['a', 'b', 'c'])

import pandas as pd
data = pd.read_csv('file.csv')

data = pd.read_csv("data.csv", index_col=0)

pd.read_excel('file.xlsx')

df.to_excel('dir/myDataFrame.xlsx',  sheet_name='Sheet2')

from sqlalchemy import create_engine
engine = create_engine('sqlite:///:memory:')
pd.read_sql("SELECT * FROM my_table;", engine)
pd.read_sql_table('my_table', engine)
pd.read_sql_query("SELECT * FROM my_table;", engine)

df.to_sql('myDf', engine)

import pandas as pd  
    
df = pd.read_csv  
  
df['Name'].head(10) 
  
# get the first element 
ser[0]

import pandas as pd  
    
df = pd.read_csv  
  
df['Name'].head(10) 
  
ser[:5]

import pandas as pd  
    
df = pd.read_csv 
  
df['Name'].head(10) 
  
ser[-5:] 

df.iloc[[0],[0]] 'Name'
df.iat([0],[0]) 'Name'

df.loc[[0], ['Label']] 'Name'
df.at([0], ['Label']) 'Name'

Access a DataFrame with a boolean index

In boolean indexing, we filter data with a boolean vector.

import pandas as pd 
   
# dictionary of lists 
dict = {'name':["name1", "name2", "name3", "name4"], 
        'degree': ["degree1", "degree2", "degree3", "degree4"], 
        'score':[1, 2, 3, 4]} 
   
df = pd.DataFrame(dict, index = [True, False, True, False]) 
   
print(df) 

s.drop(['a', 'c'])

df.drop('Value', axis=1)

df['New Column'] = 0

Rename columns in a DataFrame

df.columns = ['Column 1', 'Column 2', 'Column 3']

Series.sort_index(self, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True)

df.sort_values(by='Values')

# descending order
df.sort_values(ascending = False)

df.rank()

Retrieve rows and columns description

df.shape

df.columns

df.index

df.info()

df.count()

Get sum of values

df.sum()

# cumulative sum

df.cumsum()

s.sub(2)

s.add(2)

s.mul(2)

s.div(2)

df.min()

df.max()

df.idxmin()

df.idxmax()

df.mean()

df.median()

df.describe()

Apply a function to a dataset

f = # write function here
df.apply(f)

# apply a function by an element 

f = # write function here
df.applymap(f)

pd.merge(df1, df2, on='subject_id')

print(pd.concat([df1, df2]))

Wrapping up and resources

Now that you’re armed with the common operations and commands in Python, you can put them into practice. After all, working with real datasets is the best way to master Python and become a data analyst! There’s still a lot more to learn that we didn’t cover today such as:

• Statistics
• NumPy
• Matplotlib
• Advanced data wrangling
• Visualizations for data
• Data scraping
• Seaborn
• Scikit-learn
• and more

To get started with these essential tools of the trade, check out Educative’s course Predictive Data Analysis for Python. You’ll get hands-on practice with industry-standard examples and become fluent in the data analysis.

Keep reading about Python and data science

• Data Analysis Made Simple: Python Pandas Tutorial
• Master Algorithms with Python for Coding Interviews
• Stop Using Excel for Data Analytics: Upgrade to Python