Snowpark pandas API

The Snowpark pandas API lets you run your pandas code directly on your data in Snowflake. Just by changing the import statement and a few lines of code, you can get the same pandas-native experience you know and love with the scalability and security benefits of Snowflake. With this API, you can work with much larger datasets so you can avoid the time and expense of porting your pandas pipelines to other big data frameworks or using larger and more expensive machines. It runs workloads natively in Snowflake through transpilation to SQL, enabling it to take advantage of parallelization and the data governance and security benefits of Snowflake.

Use the Snowpark pandas API if you prioritize familiarity and ease of migration from pandas. Otherwise, use the Snowpark DataFrame API if you prefer a Spark-like workflow and are comfortable with PySpark conventions.

Benefits of using the Snowpark pandas API

  • Meeting Python developers where they are – This API offers a familiar interface to Python developers by providing a pandas-compatible layer that can run natively in Snowflake.

  • Scalable distributed pandas – This API bridges the convenience of pandas with the scalability of mature data infrastructure. pandas can now run at Snowflake speed and scale by leveraging pre-existing query optimization techniques within Snowflake. No code rewrites or complex tuning are required, so you can move from prototype to production seamlessly.

  • Security and governance – Data does not leave Snowflake’s secure platform. The Snowpark pandas API allows uniformity within data organizations on how data is accessed, and allows for easier auditing and governance.

  • No additional compute infrastructure to manage and tune – This feature leverages the Snowflake engine, and you do not need to set up or manage any additional compute infrastructure.

Getting started with Snowpark pandas

To install the Snowpark pandas API, you can use conda or pip to install the package. For more information, see Installation.

$ pip install "snowflake-snowpark-python[modin]"
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Here is an example of how you can start using Snowpark pandas.

import modin.pandas as pd

# Import the Snowpark pandas plugin for modin.
import snowflake.snowpark.modin.plugin

# Create a Snowpark session with a default connection.
from snowflake.snowpark.session import Session
session = Session.builder.create()

# Create a Snowpark pandas DataFrame with sample data.
df = pd.DataFrame([[1, 'Big Bear', 8],[2, 'Big Bear', 10],[3, 'Big Bear', None],
                    [1, 'Tahoe', 3],[2, 'Tahoe', None],[3, 'Tahoe', 13],
                    [1, 'Whistler', None],['Friday', 'Whistler', 40],[3, 'Whistler', 25]],
                    columns=["DAY", "LOCATION", "SNOWFALL"])

# Inspect the dataframe
df
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      DAY  LOCATION  SNOWFALL
0       1  Big Bear       8.0
1       2  Big Bear      10.0
2       3  Big Bear       NaN
3       1     Tahoe       3.0
4       2     Tahoe       NaN
5       3     Tahoe      13.0
6       1  Whistler       NaN
7  Friday  Whistler      40.0
8       3  Whistler      25.0
# In-place point update to fix data error.
df.loc[df["DAY"]=="Friday","DAY"]=2

# Inspect the columns after update.
# Note how the data type is updated automatically after transformation.
df["DAY"]
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0    1
1    2
2    3
3    1
4    2
5    3
6    1
7    2
8    3
Name: DAY, dtype: int64
# Drop rows with null values.
df.dropna()
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  DAY  LOCATION  SNOWFALL
0   1  Big Bear       8.0
1   2  Big Bear      10.0
3   1     Tahoe       3.0
5   3     Tahoe      13.0
7   2  Whistler      40.0
8   3  Whistler      25.0
# Compute the average daily snowfall across locations.
df.groupby("LOCATION").mean()["SNOWFALL"]
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LOCATION
Big Bear     9.0
Tahoe        8.0
Whistler    32.5
Name: SNOWFALL, dtype: float64

How Snowpark pandas compares to Snowpark DataFrames

DataFrames in Snowpark and pandas are semantically different. Snowpark DataFrames are modeled after PySpark, which operates on the original data source, gets the most recent updated data, and does not maintain order for operations. Snowpark pandas DataFrames are modeled after pandas, which operate on a snapshot of the data, maintain order during the operation, and allow for order-based positional indexing.

The Snowpark pandas DataFrame API is intended to extend the Snowpark functionality and provide a familiar interface to pandas users to facilitate easy migration and adoption, and is not a replacement for Snowpark.

You can use the following operations to do conversions between Snowpark DataFrames and Snowpark pandas DataFrames:

Operation

Input

Output

Notes

to_snowpark_pandas

Snowpark DataFrame

Snowpark pandas DataFrame

This operation assigns an implicit order to each row, and maintains this row order during the lifetime of the DataFrame.

to_snowpark

Snowpark pandas DataFrame or Snowpark pandas Series

Snowpark DataFrame

This operation does not maintain the row ordering, and the resulting Snowpark DataFrame operates on a data snapshot of the source Snowpark pandas DataFrame. Unlike Snowpark DataFrames created from the table directly, this behavior means that changes to the underlying table will not be reflected during the evaluation of the Snowpark operations.

How Snowpark pandas compares to native pandas

Snowpark pandas and native pandas have similar DataFrame APIs with matching signatures and similar semantics. Snowpark pandas provides the same API signature as native pandas (pandas 2.2.1) and provides scalable computation with Snowflake. Snowpark pandas respects the semantics described in the native pandas documentation as much as possible, but it uses the Snowflake computation and type system. However, when native pandas executes on a client machine, it uses the Python computation and type system. For information about the type mapping between Snowpark pandas and Snowflake, see Data types.

Like native pandas, Snowpark pandas also has the notion of an index and maintains row ordering. However, their distinct execution environments cause certain nuanced differences in their behavior. This section calls out the key differences to be aware of.

Snowpark pandas is best used with data which is already in Snowflake, but you can use the following operations to convert between native pandas and Snowpark pandas:

Operation

Input

Output

Notes

to_pandas

Snowpark pandas DataFrame

native pandas DataFrame

Materialize all data to the local environment. If the dataset is large, this may result in an out of memory error.

pd.DataFrame(…)

DataFrame Input

Snowpark pandas DataFrame

This should be reserved for small DataFrames. Creating a dataframe with large amounts of local data might incur performance issues due to data uploading.

session.write_pandas

pandas DataFrame

Snowflake table

The result can be subsequently loaded into Snowpark pandas with pd.read_snowflake using the table name specified in the write_pandas call.

Execution environment

  • pandas: Operates on a single machine and processes data in memory.

  • Snowpark pandas: Integrates with Snowflake, which allows for distributed computing across a cluster of machines. This integration enables handling of much larger datasets that exceed the memory capacity of a single machine. Note that using the Snowpark pandas API requires a connection to Snowflake.

Lazy vs. eager evaluation

  • pandas: Executes operations immediately and materializes results fully in memory after each operation. This eager evaluation of operations might lead to increased memory pressure as data needs to be moved extensively within a machine.

  • Snowpark pandas: Provides the same API experience as pandas. It mimics the eager evaluation model of pandas, but internally builds a lazily-evaluated query graph to enable optimization across operations.

    Fusing and transpiling operations through a query graph enables additional optimization opportunities for the underlying distributed Snowflake compute engine, which decreases both cost and end-to-end pipeline runtime compared to running pandas directly within Snowflake.

    Note

    I/O related APIs and APIs whose return value is not a Snowpark pandas object (i.e. DataFrame or Series) always evaluate eagerly. For example:

    • read_snowflake

    • to_snowflake

    • to_pandas

    • __repr__

    • df.index and series.index.

    • The dunder method, __array__ which can be called automatically by some 3rd party libraries such as scikit-learn. Calls to this method will materialize results to the local machine.

Data source and storage

  • pandas: Supports the various readers and writers listed in the pandas documentation in IO tools (text, CSV, HDF5, …).

  • Snowpark pandas: Can read and write from Snowflake tables and read local or staged csv, json, or parquet files. For more information, see IO (Read and Write).

Data types

  • pandas: Has a rich set of data types, such as integers, floats, strings, datetime types, and categorical types. It also supports user-defined data types. Data types in pandas are typically derived from the underlying data and are enforced strictly.

  • Snowpark pandas: Constrained by Snowflake type system, which maps pandas objects to SQL by translating the pandas data types to the SQL types in Snowflake. A majority of pandas types have a natural equivalent in Snowflake, but the mapping is not always one to one. In some cases, multiple pandas types are mapped to the same SQL type.

The following table lists the type mappings between pandas and Snowflake SQL:

pandas type

Snowflake type

All signed/unsigned integer types, including pandas extended integer types

NUMBER(38, 0)

All float types, including pandas extended float data types

FLOAT

bool, BooleanDtype

BOOLEAN

str, StringDtype

STRING

datetime.time

TIME

datetime.date

DATE

All timezone-naive datetime types

TIMESTAMP_NTZ

All timezone-aware datetime types

TIMESTAMP_TZ

list, tuple, array

ARRAY

dict, json

MAP

Object column with mixed data types

VARIANT

Note

Categorical, period, interval, sparse, and user-defined data types are not supported.

The following table provides the mapping of Snowflake SQL types back to Snowpark pandas types using df.dtypes:

Snowflake type

Snowpark pandas type (df.dtypes())

NUMBER (scale = 0)

int64

NUMBER (scale > 0), REAL

float64

BOOLEAN

bool

STRING, TEXT

object (str)

VARIANT, BINARY, GEOMETRY, GEOGRAPHY

object

ARRAY

object (list)

OBJECT

object (dict)

TIME

object (datetime.time)

TIMESTAMP, TIMESTAMP_NTZ, TIMESTAMP_LTZ, TIMESTAMP_TZ

datetime64[ns]

DATE

object (datetime.date)

When converting from the Snowpark pandas DataFrame to native pandas DataFrame with to_pandas(), the native pandas DataFrame will have refined data types compared to the Snowpark pandas types, which are compatible with the SQL-Python Data Type Mappings for functions and procedures.

Casting and type inference

  • pandas: Relies on NumPy and by default follows the NumPy and Python type system for implicit type casting and inference. For example, it treats booleans as integer types, so 1 + True returns 2.

  • Snowpark pandas: Maps NumPy and Python types to Snowflake types according to the preceding table, and uses the underlying Snowflake type system for implicit type casting and inference. For example, in accordance with the Logical Data Types, it does not implicitly convert booleans to integer types, so 1 + True results in a type conversion error.

Null value handling

  • pandas: In pandas versions 1.x, pandas was flexible when handling missing data, so it treated all of Python None, np.nan, pd.NaN, pd.NA, and pd.NaT as missing values. In later versions of pandas (2.2.x) these values are treated as different values.

  • Snowpark pandas: Adopts a similar approach to earlier pandas versions that treats all of the preceding values listed as missing values. Snowpark reuses NaN, NA, and NaT from pandas. But note that all these missing values are treated interchangeably and stored as SQL NULL in the Snowflake table.

Offset/frequency aliases

  • pandas: Date offsets in pandas changed in version 2.2.1. The single-letter aliases 'M', 'Q', 'Y', and others have been deprecated in favor of two-letter offsets.

  • Snowpark pandas: Exclusively uses the new offsets described in the pandas time series documentation.

API coverage

Snowpark pandas currently supports about 45 percent of the total native pandas API, and support for additional API operations is actively in development.

For a current list of supported operations, see the following tables in the Snowpark pandas API reference:

Prerequisites

Python 3.9, 3.10 or 3.11, modin version 0.28.1, and pandas version 2.2.1 are required.

Installation

To install the Snowpark pandas API:

  1. Change to your project directory and activate your Python virtual environment.

    Note

    The API is under active development, so we recommend installing it in a Python virtual environment rather than system-wide. This practice allows each project you create to use a specific version, insulating you from changes in future versions.

    You can create a Python virtual environment for a particular Python version using tools like Anaconda, Miniconda, or virtualenv.

    For example, to use conda to create a Python 3.9 virtual environment, type:

    conda create --name snowpark_pandas python=3.9
    conda activate snowpark_pandas
    
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    Note

    If you previously installed an older version of Snowpark pandas using Python 3.8 and pandas 1.5.3, you will need to upgrade your Python and pandas versions as described above. Follow the steps to create a new environment with Python 3.9, 3.10, or 3.11.

  2. Install the Snowpark Python library with Modin.

    pip install "snowflake-snowpark-python[modin]"
    
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    or

    conda install snowflake-snowpark-python modin==0.28.1
    
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Note

Make sure snowflake-snowpark-python version 1.17.0 or later is installed.

Authenticating to Snowflake

Before using the Snowpark pandas API, you must establish a session with the Snowflake database. You can use a config file to choose the connection parameters for your session or you can enumerate them in your code. For more information, see Creating a Session for Snowpark Python. If a unique active Snowpark Python session exists, the Snowpark pandas API will automatically use it. For example:

import modin.pandas as pd
import snowflake.snowpark.modin.plugin
from snowflake.snowpark import Session

CONNECTION_PARAMETERS = {
    'account': '<myaccount>',
    'user': '<myuser>',
    'password': '<mypassword>',
    'role': '<myrole>',
    'database': '<mydatabase>',
    'schema': '<myschema>',
    'warehouse': '<mywarehouse>',
}
session = Session.builder.configs(CONNECTION_PARAMETERS).create()

# Snowpark pandas will automatically pick up the Snowpark session created above.
# It will use that session to create new dataframes.
df = pd.DataFrame([1, 2])
df2 = pd.read_snowflake('CUSTOMER')
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The pd.session is a Snowpark session, so you can do anything with it that you can do with any other Snowpark session. For example, you can use it to execute an arbitrary SQL query, which results in a Snowpark DataFrame as per the Session API, but take note that the results of this will be a Snowpark DataFrame, not a Snowpark pandas DataFrame.

# pd.session is the session that Snowpark pandas is using for new dataframes.
# In this case it is the same as the Snowpark session that we've created.
assert pd.session is session

# Run SQL query with returned result as Snowpark DataFrame
snowpark_df = pd.session.sql('select * from customer')
snowpark_df.show()
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Alternatively, you can configure your Snowpark connection parameters in a configuration file. This eliminates the need to enumerate connection parameters in your code, allowing you to write your Snowpark pandas code almost as you would normally write pandas code. To achieve this, create a configuration file located at ~/.snowflake/connections.toml that looks something like this:

default_connection_name = "default"

[default]
account = "<myaccount>"
user = "<myuser>"
password = "<mypassword>"
role="<myrole>"
database = "<mydatabase>"
schema = "<myschema>"
warehouse = "<mywarehouse>"
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Then in the code, you only need to use snowflake.snowpark.Session.builder.create() to create a session using these credentials.

import modin.pandas as pd
import snowflake.snowpark.modin.plugin
from snowflake.snowpark import Session

# Session.builder.create() will create a default Snowflake connection.
Session.builder.create()
# create a DataFrame.
df = pd.DataFrame([[1, 2], [3, 4]])
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You can also create multiple Snowpark sessions, then assign one of them to Snowpark pandas. Snowpark pandas only uses one session, so you have to explicitly assign one of the sessions to Snowpark pandas with pd.session = pandas_session.

import modin.pandas as pd
import snowflake.snowpark.modin.plugin
from snowflake.snowpark import Session

pandas_session = Session.builder.configs({"user": "<user>", "password": "<password>", "account": "<account1>").create()
other_session = Session.builder.configs({"user": "<user>", "password": "<password>", "account": "<account2>").create()
pd.session = pandas_session
df = pd.DataFrame([1, 2, 3])
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The following example shows that trying to use Snowpark pandas when there is no active Snowpark session will raise a SnowparkSessionException with an error like “Snowpark pandas requires an active snowpark session, but there is none.” Once you create a session, you can use Snowpark pandas. For example:

import modin.pandas as pd
import snowflake.snowpark.modin.plugin

df = pd.DataFrame([1, 2, 3])
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The following example shows that trying to use Snowpark pandas when there are multiple active Snowpark sessions will cause a SnowparkSessionException with a message like, “There are multiple active snowpark sessions, but you need to choose one for Snowpark pandas.”

import modin.pandas as pd
import snowflake.snowpark.modin.plugin
from snowflake.snowpark import Session

pandas_session = Session.builder.configs({"user": "<user>", "password": "<password>", "account": "<account1>"}).create()
other_session = Session.builder.configs({"user": "<user>", "password": "<password>", "account": "<account2>"}).create()
df = pd.DataFrame([1, 2, 3])
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Note

You must set the session used for a new Snowpark pandas DataFrame or Series via modin.pandas.session However, joining or merging DataFrames created with different sessions is not supported, so you should avoid repeatedly setting different sessions and creating DataFrames with different sessions in a workflow.

API Reference

To view the Snowpark pandas API reference, see Snowpark pandas API.

Limitations

Snowpark pandas has the following limitations:

  • Snowpark pandas provides no guarantee of compatibility with OSS third-party libraries. Starting with version 1.14.0a1, however, Snowpark pandas introduces limited compatibility for NumPy, specifically for np.where usage. For more information, see NumPy Interoperability.

    When calling third-party library APIs with a Snowpark pandas dataframe, Snowflake recommends that you convert the Snowpark pandas dataframe to a pandas dataframe by calling to_pandas() before passing the dataframe to the third-party library call.

  • Snowpark pandas is not integrated with Snowpark ML. When using Snowpark ML, we recommend that you convert the Snowpark pandas dataframe to a Snowpark dataframe using to_snowpark() before calling Snowpark ML.

  • Lazy Index objects are not supported. When dataframe.index is called, it returns a native pandas Index object, which requires pulling all data to the client side.

  • Not all pandas APIs have a distributed implementation yet in Snowpark pandas. For unsupported APIs, NotImplementedError is thrown. Operations that have no distributed implementation fall back to a stored procedure. For information about supported APIs, refer to the API reference documentation.

  • Snowpark pandas requires a specific pandas version. Snowpark pandas requires pandas 2.2.1, and only provides compatibility with pandas 2.2.1.

  • Snowpark pandas provides fast and zero copy clone capability while creating DataFrames from Snowflake tables. However, several table types do not support zero copy clone capability and will materialize the data, which might be slow for large tables. Some examples of such tables include Hybrid tables, Iceberg tables, External tables, and tables from shared databases.

Troubleshooting

This section describes troubleshooting tips when using Snowpark pandas.

  • When troubleshooting, try running the same operation on a native pandas dataframe (or a sample) to see if the same error persists with pandas. This approach might provide hints on how to fix your query. For example:

    df = pd.DataFrame({"a": [1,2,3], "b": ["x", "y", "z"]})
    # Running this in Snowpark pandas throws an error
    df["A"].sum()
    # Convert to pandas dataframe
    pandas_df = df.to_pandas()
    # Run the same operation. KeyError tells that the column reference is incorrect
    pandas_df["A"].sum()
    # Fix the column reference to get Snowpark pandas query working
    df["a"].sum()
    
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  • If you have a long-running notebook opened, note that by default Snowflake sessions timeout after the session is idle for 240 minutes (4 hours). When the session expires, you will get the following error if you run additional Snowpark pandas queries: “Authentication token has expired. The user must authenticate again.” At this point, you must re-establish the connection to Snowflake again. This may result in loss of any unpersisted session variable. For more information about how to configure the session idle timeout parameter, see Session Policies.

Best practices

This section describes best practices to follow when using Snowpark pandas.

  • Avoid using iterative code patterns, such as for loops, iterrows, and iteritems. Iterative code patterns quickly increase the generated query complexity. Let Snowpark pandas perform the data distribution and computation parallelization rather than the client code. When it comes to iterative code patterns, try to look for operations that can be performed on the whole dataframe and use the corresponding operations instead.

for i in np.arange(0, 50):
  if i % 2 == 0:
    data = pd.concat([data, pd.DataFrame({'A': i, 'B': i + 1}, index=[0])], ignore_index=True)
  else:
    data = pd.concat([data, pd.DataFrame({'A': i}, index=[0])], ignore_index=True)

# Instead of creating one dataframe per row and concatenating them,
# try to directly create the dataframe out of the data, like this:

data = pd.DataFrame(
      {
          "A": range(0, 50),
          "B": [i + 1 if i % 2 == 0 else None for i in range(50)],
      },
)
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  • Avoid calling apply, applymap and transform, which are eventually implemented with UDFs or UDTFs, which might not be as performant as regular SQL queries. If the function applied has an equivalent dataframe or series operation, use that operation instead. For example, instead of df.groupby('col1').apply('sum'), directly call df.groupby('col1').sum().

  • Call to_pandas() before passing the dataframe or series to a third-party library call. Snowpark pandas does not provide a compatibility guarantee with third-party libraries.

  • Use a materialized regular Snowflake table to avoid extra I/O overhead. Snowpark pandas works on top of a data snapshot that only works for regular tables. For other types, including external table, views, and iceberg tables, a temporary table is created before taking the snapshot, which introduces extra materialization overhead.

  • Double check the result type before proceeding to other operations, and do explicit type casting with astype if needed.

    Due to limited type inference capability, if no type hint is given, df.apply will return results of object (variant) type even if the result contains all integer values. If other operations require the dtype to be int, you can do an explicit type casting by calling the astype method to correct the column type before you continue.

  • Avoid calling APIs that require evaluation and materialization if not necessary.

    APIs that don’t return Series or Dataframe require eager evaluation and materialization to produce the result in the correct type. Reduce calls to those APIs to minimize unnecessary evaluations and materialization.

  • Avoid calling np.where(<cond>, <scalar>, n) on large datasets. The <scalar> will be broadcast to a DataFrame the size of <cond>, which may be slow.

  • When working with iteratively built queries, use df.cache_result to materialize intermediate results in order to improve the latency and reduce complexity of the overall query. For example:

    df = pd.read_snowflake('pandas_test')
    df2 = pd.pivot_table(df, index='index_col', columns='pivot_col') # expensive operation
    df3 = df.merge(df2)
    df4 = df3.where(df2 == True)
    
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    In the above example, the query to produce df2 is expensive to compute, and is reused in the creation of both df3 and df4. Materializing df2 into a temporary table (making subsequent operations involving df2 a table scan instead of a pivot) can reduce the overall latency of the code block:

    df = pd.read_snowflake('pandas_test')
    df2 = pd.pivot_table(df, index='index_col', columns='pivot_col') # expensive operation
    df2.cache_result(inplace=True)
    df3 = df.merge(df2)
    df4 = df3.where(df2 == True)
    
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Examples

Here is a code example with pandas operations. We start with a Snowpark pandas dataframe named pandas_test, which contains three columns: COL_STR, COL_FLOAT, and COL_INT. To view the notebook associated with these examples, see the Snowpark pandas API examples in the Snowflake-Labs repository.

import modin.pandas as pd
import snowflake.snowpark.modin.plugin

from snowflake.snowpark import Session

CONNECTION_PARAMETERS = {
    'account': '<myaccount>',
    'user': '<myuser>',
    'password': '<mypassword>',
    'role': '<myrole>',
    'database': '<mydatabase>',
    'schema': '<myschema>',
    'warehouse': '<mywarehouse>',
}
session = Session.builder.configs(CONNECTION_PARAMETERS).create()

df = pd.DataFrame([['a', 2.1, 1],['b', 4.2, 2],['c', 6.3, None]], columns=["COL_STR", "COL_FLOAT", "COL_INT"])

df
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  COL_STR  COL_FLOAT  COL_INT
0       a        2.1        1
1       b        NaN        2
2       c        6.3        3

We save the dataframe as a Snowflake table named pandas_test which we will use throughout our examples.

df.to_snowflake("pandas_test", if_exists='replace',index=False)
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Next, we create a dataframe from the Snowflake table. We drop the column COL_INT and then save the result back to Snowflake with a column named row_position.

# Create a dataframe out of a Snowflake table.
df = pd.read_snowflake('pandas_test')

df.shape
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(3, 3)
df.head(2)
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  COL_STR  COL_FLOAT  COL_INT
0       a        2.1        1
1       b        NaN        2
df.dropna(subset=["COL_FLOAT"], inplace=True)

df
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  COL_STR  COL_FLOAT  COL_INT
0       a        2.1        1
2       c        6.3        2
df.shape
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(2, 3)
df.dtypes
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COL_STR       object
COL_FLOAT    float64
COL_INT        int64
dtype: object
# Save the result back to Snowflake with a row_pos column.
df.reset_index(drop=True).to_snowflake('pandas_test2', if_exists='replace', index=True, index_label=['row_pos'])
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You end up with a new table, pandas_test2, which looks like this:

row_pos  COL_STR  COL_FLOAT  COL_INT
0          a          2.0          1
1          b          4.0          2

IO (Read and Write)

# Reading and writing to Snowflake
df = pd.DataFrame({"fruit": ["apple", "orange"], "size": [3.4, 5.4], "weight": [1.4, 3.2]})
df.to_snowflake("test_table", if_exists="replace", index=False )

df_table = pd.read_snowflake("test_table")


# Generate sample CSV file
with open("data.csv", "w") as f:
    f.write('fruit,size,weight\napple,3.4,1.4\norange,5.4,3.2')
# Read from local CSV file
df_csv = pd.read_csv("data.csv")

# Generate sample JSON file
with open("data.json", "w") as f:
    f.write('{"fruit":"apple", "size":3.4, "weight":1.4},{"fruit":"orange", "size":5.4, "weight":3.2}')
# Read from local JSON file
df_json = pd.read_json('data.json')

# Upload data.json and data.csv to Snowflake stage named @TEST_STAGE
# Read CSV and JSON file from stage
df_csv = pd.read_csv('@TEST_STAGE/data.csv')
df_json = pd.read_json('@TEST_STAGE/data.json')
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For more information, see Input/Output.

Indexing

df = pd.DataFrame({"a": [1,2,3], "b": ["x", "y", "z"]})
df.columns
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Index(['a', 'b'], dtype='object')
df.index
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Index([0, 1, 2], dtype='int8')
df["a"]
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0    1
1    2
2    3
Name: a, dtype: int8
df["b"]
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0    x
1    y
2    z
Name: b, dtype: object
df.iloc[0,1]
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'x'
df.loc[df["a"] > 2]
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a  b
2  3  z
df.columns = ["c", "d"]
df
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   c  d
0  1  x
1  2  y
2  3  z
df = df.set_index("c")
df
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   d
c
1  x
2  y
3  z
df.rename(columns={"d": "renamed"})
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    renamed
c
1       x
2       y
3       z

Missing values

import numpy as np
df = pd.DataFrame([[np.nan, 2, np.nan, 0],
                [3, 4, np.nan, 1],
                [np.nan, np.nan, np.nan, np.nan],
                [np.nan, 3, np.nan, 4]],
                columns=list("ABCD"))
df
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     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0
df.isna()
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       A      B     C      D
0   True  False  True  False
1  False  False  True  False
2   True   True  True   True
3   True  False  True  False
df.fillna(0)
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     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  0.0
3  0.0  3.0  0.0  4.0
df.dropna(how="all")
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     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
3  NaN  3.0 NaN  4.0

Type conversion

df = pd.DataFrame({"int": [1,2,3], "str": ["4", "5", "6"]})
df
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   int str
0    1   4
1    2   5
2    3   6
df_float = df.astype(float)
df_float
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   int  str
0  1.0  4.0
1  2.0  5.0
2  3.0  6.0
df_float.dtypes
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int    float64
str    float64
dtype: object
pd.to_numeric(df.str)
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0    4.0
1    5.0
2    6.0
Name: str, dtype: float64
df = pd.DataFrame({'year': [2015, 2016],
                'month': [2, 3],
                'day': [4, 5]})
pd.to_datetime(df)
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0   2015-02-04
1   2016-03-05
dtype: datetime64[ns]

Binary operations

df_1 = pd.DataFrame([[1,2,3],[4,5,6]])
df_2 = pd.DataFrame([[6,7,8]])
df_1.add(df_2)
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    0    1     2
0  7.0  9.0  11.0
1  NaN  NaN   NaN
s1 = pd.Series([1, 2, 3])
s2 = pd.Series([2, 2, 2])
s1 + s2
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0    3
1    4
2    5
dtype: int64
df = pd.DataFrame({"A": [1,2,3], "B": [4,5,6]})
df["A+B"] = df["A"] + df["B"]
df
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   A  B  A+B
0  1  4    5
1  2  5    7
2  3  6    9

Aggregation

df = pd.DataFrame([[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9],
                [np.nan, np.nan, np.nan]],
                columns=['A', 'B', 'C'])
df.agg(['sum', 'min'])
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        A     B     C
sum  12.0  15.0  18.0
min   1.0   2.0   3.0
df.median()
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A    4.0
B    5.0
C    6.0
dtype: float64

Merge

df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
                    'value': [1, 2, 3, 5]})
df1
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  lkey  value
0  foo      1
1  bar      2
2  baz      3
3  foo      5
df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
                    'value': [5, 6, 7, 8]})
df2
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  rkey  value
0  foo      5
1  bar      6
2  baz      7
3  foo      8
df1.merge(df2, left_on='lkey', right_on='rkey')
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  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  bar        2  bar        6
3  baz        3  baz        7
4  foo        5  foo        5
5  foo        5  foo        8
df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
                'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
df
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  key   A
0  K0  A0
1  K1  A1
2  K2  A2
3  K3  A3
4  K4  A4
5  K5  A5
other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
                    'B': ['B0', 'B1', 'B2']})
df.join(other, lsuffix='_caller', rsuffix='_other')
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  key_caller   A key_other     B
0         K0  A0        K0    B0
1         K1  A1        K1    B1
2         K2  A2        K2    B2
3         K3  A3      None  None
4         K4  A4      None  None
5         K5  A5      None  None

Groupby

df = pd.DataFrame({'Animal': ['Falcon', 'Falcon','Parrot', 'Parrot'],
               'Max Speed': [380., 370., 24., 26.]})

df
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   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
df.groupby(['Animal']).mean()
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        Max Speed
Animal
Falcon      375.0
Parrot       25.0

For more information, see GroupBy.

Pivot

df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
                        "bar", "bar", "bar", "bar"],
                "B": ["one", "one", "one", "two", "two",
                        "one", "one", "two", "two"],
                "C": ["small", "large", "large", "small",
                        "small", "large", "small", "small",
                        "large"],
                "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
                "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
df
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     A    B      C  D  E
0  foo  one  small  1  2
1  foo  one  large  2  4
2  foo  one  large  2  5
3  foo  two  small  3  5
4  foo  two  small  3  6
5  bar  one  large  4  6
6  bar  one  small  5  8
7  bar  two  small  6  9
8  bar  two  large  7  9
pd.pivot_table(df, values='D', index=['A', 'B'],
                   columns=['C'], aggfunc="sum")
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    C    large  small
A   B
bar one    4.0      5
    two    7.0      6
foo one    4.0      1
    two    NaN      6
df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
                'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
                'baz': [1, 2, 3, 4, 5, 6],
                'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
df
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   foo bar  baz zoo
0  one   A    1   x
1  one   B    2   y
2  one   C    3   z
3  two   A    4   q
4  two   B    5   w
5  two   C    6   t

MultiIndex

arrays = [
    ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
    ["one", "two", "one", "two", "one", "two", "one", "two"],
]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
index
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MultiIndex([('bar', 'one'),
            ('bar', 'two'),
            ('baz', 'one'),
            ('baz', 'two'),
            ('foo', 'one'),
            ('foo', 'two'),
            ('qux', 'one'),
            ('qux', 'two')],
            names=['first', 'second'])
import numpy as np
pd.Series(np.random.randn(8), index=index)
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first  second
bar    one      -0.915158
       two      -0.063246
baz    one       0.454371
       two      -0.411334
foo    one      -1.345568
       two      -0.862540
qux    one       1.757797
       two      -1.525293
dtype: float64

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