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, data science and System Design#

• Data Analysis Made Simple: Python Pandas Tutorial
• Master Algorithms with Python for Coding Interviews
• Stop Using Excel for Data Analytics: Upgrade to Python
• System Design interview
• System Design Interview questions
• System Design Primer