> ## Content Index > Fetch the complete content index at: https://datascientyst.com/llms.txt > Use this file to discover other available public pages before exploring further. # Pandas Cheat Sheet for Data Science - URL: https://datascientyst.com/pandas-cheat-sheet-for-data-science/ - Published: 2022-04-20T10:31:04.000Z - Updated: 2023-03-01T05:21:33.000Z - Author: John D K - Tags: Cheat Sheet A handy **Pandas Cheat Sheet** useful for the aspiring data scientists and contains ready-to-use codes for data wrangling. The **cheat sheet summarize the most commonly used Pandas features and APIs**. This cheat sheet will act as a **crash course for Pandas beginners** and help you with various fundamentals of Data Science. It can be used by experienced users as a quick reference. - [Pandas API Reference](https://pandas.pydata.org/pandas-docs/stable/reference/index.html?ref=datascientyst.com#api) - [Pandas User Guide](https://pandas.pydata.org/pandas-docs/stable/user%5Fguide/index.html?ref=datascientyst.com#user-guide) - [Data Wrangling with Pandas Cheat Sheet](https://pandas.pydata.org/Pandas%5FCheat%5FSheet.pdf?ref=datascientyst.com) ## Pandas setup Pandas is open source package for data science/data analysis ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_basics.png) `pip install pandas` Install Pandas library `import pandas as pd` Import Pandas `df = (pd.melt(df) .rename(columns={'variable': 'var'}) .query('val >= 200') )` Method Chaining `df[(df['col_1']=='a')&(df['col_2']>=10)]` Pandas syntax ## Data Structures Pandas has two data structures: Series (1 dimension) and DataFrame (multidimensional) ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_data_structures-1.png) ` s = pd.Series(['a', 'b', 'c'], index=[0 , 1, 2])` Create new Series `df = pd.DataFrame( {'col_1': [11, 12, 13], 'col_2': [21, 22, 23], 'col_3': [31, 32, 33]}, index=[0, 1, 2])` Create new DataFrame ## Read Import data from CSV, Excel, JSON, SQL, HTML, web ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_read.png) `pd.read_csv(filename)` From a CSV file `pd.read_csv(filename, header=None, nrows=5)` From a CSV file with parameters `pd.read_excel(filename)` From an Excel file `pd.read_sql(query, connection_object)` Reads from a SQL table/database `pd.read_json(json_string)` Reads from a JSON formatted string, URL or file. ## Write Write data to CSV, Excel, JSON, HTML ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_write.png) `df.to_csv(filename)` Writes to a CSV file `df.to_excel(filename)` Writes to an Excel file `df.to_json(filename)` Writes to a file in JSON format `df.to_html(filename)` Saves as an HTML table ## Inspect Data View stats, samples and summary of the data ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_info.png) `df.head(n)` First n rows `df.tail(n)` Last n rows `df.shape` Number of rows and columns `df.info()` Index, Datatype and Memory information `df.describe()` Summary statistics for numerical columns `s.value_counts(dropna=False)` (Series) Views unique values and counts `df.sample(n)` Randomly select n rows. `df.nlargest(n, 'col_1')` Select and order top n entries for column `df.nsmallest(n, 'col_1')` Select and order bottom n entries. `df.quantile([0.25,0.75])` Quantiles of each object ## Select Select data by index, by label, get subset ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_select.png) `s.loc[0]` (Series) Select by index `s.iloc[0]` (Series) Select by position `df['col_1']` Get single column as Series `df[['col_1', 'col_2']]` Get multiple columns as a DataFrame `df.iloc[0,:]` Select first row from DataFrame `df.iloc[0,0]` First element of first column `df.loc[df['col_1'] > 10, ['col_1', 'col_2']]` Select rows meeting logical condition, and only the specific columns `df.iat[1, 2] ` Access single value by index `df.at[3, 'col_2']` Access single value by label ## Add rows/columns Add new values to existing DataFrame ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_add.png) `df['new col'] = df['col'] * 100` Add new column based on other column `df['new col'] = False` Add new column single value `df.loc[-1] = [1, 2, 3]` Add new row at the end of DataFrame `df.append(df2, ignore_index = True)` add rows from DataFrame to existing DataFrame ## Drop rows/columns/nan Drop data from DataFrame ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_drop.png) `s.drop([0, 1])` (Series) Drop values from Series by index (row axis) `df.drop('col_1' , axis=1) ` Drop column by name col\_1 (column axis) `df.dropna()` Drops all rows that contain null values `df.dropna(axis=1)` Drops all columns that contain null values `df.dropna(axis=1,thresh=n)` Drops all rows have have less than n non null values ## Sort values/index Sort and rank values/index by one or multiple criteria ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_sort.png) `df.sort_values(by='col_1', ascending=False)` Sort values by column, ascending order `df.sort_values(by=['col_1', 'col_2'])` Sort values by columns `df.sort_index(ascending=False)` Sort object by labels (along an axis) in descending order `df.sort_values(by=[('col_1', 'col_2')])` Sort multindex by multiple levels `df.reset_index()` Reset the index of the DataFrame, moving index to columns ## Filter Filter data based on multiple criteria ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_filter.png) `df[df['col_1'] > 100]` Values greater than X `df[(df['col_1']=='a')&(df['col_2']>=10)]` Filter Multiple Conditions - & - and; | - or `df[df['date'] > '2022-02-22']` Date filtering `df[df['date'].dt.month == 2]` Filter with dt attributes `df[df['col_1'].str.contains('pan*', regex=True)]` Filter by regex `df[df['col_1'].isin(['pan', 'das'])]` Filter based on list of values `df.query('col_1 > 100')` Filter by queries `df.query('col_1 > 100 and col_2 = 0')` Filter by multiple queries ## Group by Group by and summarize data ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_groupby.png) `df.groupby('col_1')` Group by single column - return pandas.core.groupby.DataFrameGroupBy `df.groupby(['col_1', 'col_2'])` Group by multiple columns `df.groupby('col_1').groups` View groups `df.groupby('col_1').get_group(1)` Get group `df.groupby('col_1').count()` Get count per groups `df.groupby('col_1').agg([np.sum, np.mean])` Apply multiple agg functions on group `df.groupby('col_1').filter(lambda x: len(x) >= 5)` Filter groups `df.groupby('col_1').agg('count')` Aggregate group using function. `df.groupby('col_1').rank(method='dense')` Compute numerical data ranks (1 through n) along axis. ## Convert Convert to date, string, numeric ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_convert.png) `df['points'].astype(str)` convert to string `df['col_1'].astype('int64')` convert to int64 `df['col_1'].astype(float)` convert to float `pd.to_numeric(df['col_1'], errors='coerce')` convert to numeric `pd.to_datetime(df['date'], format='%Y-%m-%d')` convert string to date `pd.DataFrame(df['Values'].tolist(), columns=['col_1', 'col_1'])` Split column list to multiple columns `df['col_1'].apply(pd.Series)` Expand Series of dictionnaries ## Merge & Concat Merging, joniing and concatenating 2 and more DataFrames ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_merge.png) `df1.append(df2)` Adds the rows in df1 to the end of df2 (columns should be identical) `pd.concat([df1, df2],axis=1)` Adds the columns in df1 to the end of df2 (rows should be identical) `df1.join(df2,on=col1,how='inner')` SQL-style joins the columns in df1 with the columns on df2 where the rows for col have identical values. how can be one of 'left', 'right', 'outer', 'inner' ## Apply Applying functions to a column or DataFrame; lambda functions ![](https://datascientyst.com/content/images/2022/04/pandas_cheat_sheet_apply.png) `def calc(x): return x + 1 df.apply(calc, axis = 1)` Apply function to DataFrame `df[['col_1','col_2']].apply(calc)` Apply to multiple columns `df.apply(lambda x: x * -1 if x < 0 else x)` Apply lambda ![](https://datascientyst.com/content/images/2022/06/pandas_cheatsheet.png)