Pandas is an essential library for data manipulation and analysis in Python, renowned for its capabilities to handle and process large datasets efficiently. Whether you're a beginner or a seasoned data professional, Pandas provides flexible data structures and functions that make data analysis seamless and straightforward. This cheat sheet is a quick reference guide for some of the most common operations you might perform with the Pandas library.
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Importing Data
| Action |
Definition |
Example Code Snippet |
| Import |
Standard import statement to bring Pandas into the script |
import pandas as pd |
| Read_CSV |
Reads a comma-separated values (CSV) file into DataFrame |
df = pd.read_csv('file.csv') |
| Read_Table |
Reads a general delimited file into DataFrame |
df = pd.read_table('file.txt') |
| Read_Excel |
Reads an Excel file into DataFrame |
df = pd.read_excel('file.xlsx') |
| Read_SQL |
Reads SQL query or database table into DataFrame |
df = pd.read_sql('SELECT * FROM table', conn) |
| Read_JSON |
Reads a JSON formatted string into DataFrame |
df = pd.read_json('file.json') |
| Read_HTML |
Reads HTML tables into DataFrame |
df = pd.read_html('url') |
| Clipboard |
Reads text from the clipboard into DataFrame |
df = pd.read_clipboard() |
Exporting Data
| Action |
Definition |
Example Code Snippet |
| To_CSV |
Writes DataFrame to a comma-separated values (CSV) file |
df.to_csv('file.csv') |
| To_Excel |
Writes DataFrame to an Excel file |
df.to_excel('file.xlsx') |
| To_SQL |
Writes DataFrame to a SQL database |
df.to_sql('table_name', conn) |
| To_JSON |
Writes DataFrame to a JSON formatted string |
df.to_json('file.json') |
| To_HTML |
Writes DataFrame to HTML tables |
df.to_html('file.html') |
| To_Clipboard |
Writes DataFrame to the clipboard |
df.to_clipboard() |
Create Test Objects
| Action |
Definition |
Example Code Snippet |
| Dataframe |
Constructs a DataFrame object |
df = pd.DataFrame(data) |
| Series |
Constructs a Series object |
s = pd.Series(data) |
| Index |
Constructs an Index object |
index = pd.Index(data) |
Working with DataFrames
DataFrame Basics
| Action |
Definition |
Example Code Snippet |
| Return Dimensions of a DataFrame |
Gets shape of DataFrame |
df.shape |
| Read CSV file into a DataFrame |
Reads CSV and returns DataFrame object |
df = pd.read_csv('file.csv') |
| Return the data type of each column |
Returns data types of columns in DataFrame |
df.dtypes |
Selecting DataFrame Values
| Action |
Definition |
Example Code Snippet |
| Select the rank column from f500 |
Selects a specific column from DataFrame |
f500['rank'] |
| Select the first 3 rows from f500 |
Slices the DataFrame |
f500.head(3) |
LOC
| Action |
Definition |
Example Code Snippet |
| LOC |
Access a group of rows and columns by labels |
df.loc[row_index, 'column_name'] |
ILOC
| Action |
Definition |
Example Code Snippet |
| ILOC |
Access a group of rows and columns by integer index |
df.iloc[row_index, col_index] |
Boolean Masks
| Action |
Definition |
Example Code Snippet |
| Boolean Masks |
Filters DataFrame based on a condition |
df[df['column'] > value] |
Boolean Operators
| Action |
Definition |
Example Code Snippet |
| Boolean Operators |
Combines multiple conditions for filtering |
df[(df['column'] > value) & (df['column'] < value2)] |
Graphs
| Action |
Definition |
Example Code Snippet |
| Generate a frequent table from a series object |
Counts unique values in Series |
series.value_counts() |
| Generate a sorted frequency table from series object |
Counts and sorts unique values |
series.value_counts().sort_values() |
| Generate a vertical bar plot from a series object |
Plots bar chart from Series |
series.plot.bar() |
| Generate a horizontal bar plot from a series object |
Plots horizontal bar chart from Series |
series.plot.barh() |
| Generate a line plot from a DataFrame object |
Plots line chart from DataFrame |
df.plot.line() |
| Generate a scatter plot from a DataFrame object |
Plots scatter chart |
df.plot.scatter(x='col1', y='col2') |
Data Cleaning
| Action |
Definition |
Example Code Snippet |
| Columns |
Access columns of DataFrame as attributes |
df.columns |
| Isnull |
Detects missing values |
df.isnull() |
| Notnull |
Detects non-missing values |
df.notnull() |
| Dropna |
Removes missing values |
df.dropna() |
| Fillna |
Fills missing values |
df.fillna(value) |
| Astype |
Converts data type of a DataFrame column |
df['col'] = df['col'].astype('int') |
| Replace |
Replaces values |
df.replace(to_replace, value) |
| Rename |
Renames DataFrame columns |
df.rename(columns={'old': 'new'}) |
| Set_index |
Sets DataFrame index |
df.set_index('col') |
| Finding correlation |
Computes pairwise correlation of columns |
df.corr() |
| Converting a column to datetime |
Converts column to datetime format |
df['date'] = pd.to_datetime(df['date']) |
Join and Combine
| Action |
Definition |
Example Code Snippet |
| Append |
Appends rows of other DataFrame to the end |
df.append(other_df) |
| Concat |
Concatenate pandas objects along a particular axis |
pd.concat([df1, df2]) |
| Join |
Joins columns of another DataFrame |
df.join(other_df) |
Statistics
| Action |
Definition |
Example Code Snippet |
| Describe |
Generates descriptive statistics |
df.describe() |
| Mean |
Computes mean of DataFrame |
df.mean() |
| Corr |
Computes pairwise correlation of columns |
df.corr() |
| Count |
Returns the number of non-NA/null observations |
df.count() |
| Max |
Returns the maximum of DataFrame values |
df.max() |
| Min |
Returns the minimum of DataFrame values |
df.min() |
| Median |
Computes the median of DataFrame columns |
df.median() |
| STD |
Computes the standard deviation of DataFrame columns |
df.std() |
This cheat sheet covers the essential functions and commands you'll need when working with Pandas in Python, providing quick references to keep your data analysis workflows smooth and productive.Â
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