diff --git a/docs/installation.md b/docs/installation.md
index fd68a4e3d..f87db1d7a 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -25,11 +25,11 @@ pip install -U ipyvizzu
 ```
 
 !!! note
-    `ipyvizzu` has some extra dependencies such as `pandas`, `numpy` and
-    `fugue`.
+    `ipyvizzu` can be used with some extra dependencies such as `pandas`,
+    `pyspark`, `numpy` and `fugue`.
 
     For example if you would like to work with `pandas` `DataFrame` and
-    `ipyvizzu`, you can install `pandas` as an extra:
+    `ipyvizzu`, you should install `pandas` as an extra:
 
     ```sh
     pip install ipyvizzu[pandas]
diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md
index d03fe455c..a54981c5f 100644
--- a/docs/tutorial/data.md
+++ b/docs/tutorial/data.md
@@ -50,7 +50,7 @@ There are multiple ways you can add data to `ipyvizzu`.
 
 Use
 [`add_df`](../reference/ipyvizzu/animation.md#ipyvizzu.animation.Data.add_df)
-method for adding data frame to
+method for adding `pandas` DataFrame to
 [`Data`](../reference/ipyvizzu/animation.md#ipyvizzu.animation.Data).
 
 ```python
@@ -143,12 +143,12 @@ df = pd.DataFrame(
 )
 
 data = Data()
+data.add_df_index(df, column_name="IndexColumnName")
 data.add_df(df)
-data.add_df_index(df, name="IndexColumnName")
 ```
 
 !!! note
-    If you want to work with `pandas` `DataFrame` and `ipyvizzu`, you need to
+    If you want to work with `pandas` DataFrame and `ipyvizzu`, you need to
     install `pandas` or install it as an extra:
 
     ```sh
@@ -320,6 +320,60 @@ data.add_df(df)
     You'll need to adjust the SQL query and the database connection parameters
     to match your specific use case.
 
+### Using `pyspark` DataFrame
+
+Use
+[`add_df`](../reference/ipyvizzu/animation.md#ipyvizzu.animation.Data.add_df)
+method for adding `pyspark` DataFrame to
+[`Data`](../reference/ipyvizzu/animation.md#ipyvizzu.animation.Data).
+
+```python
+from pyspark.sql import SparkSession
+from pyspark.sql.types import (
+    StructType,
+    StructField,
+    StringType,
+    IntegerType,
+)
+from ipyvizzu import Data
+
+
+spark = SparkSession.builder.appName("ipyvizzu").getOrCreate()
+spark_schema = StructType(
+    [
+        StructField("Genres", StringType(), True),
+        StructField("Kinds", StringType(), True),
+        StructField("Popularity", IntegerType(), True),
+    ]
+)
+spark_data = [
+    ("Pop", "Hard", 114),
+    ("Rock", "Hard", 96),
+    ("Jazz", "Hard", 78),
+    ("Metal", "Hard", 52),
+    ("Pop", "Smooth", 56),
+    ("Rock", "Experimental", 36),
+    ("Jazz", "Smooth", 174),
+    ("Metal", "Smooth", 121),
+    ("Pop", "Experimental", 127),
+    ("Rock", "Experimental", 83),
+    ("Jazz", "Experimental", 94),
+    ("Metal", "Experimental", 58),
+]
+df = spark.createDataFrame(spark_data, spark_schema)
+
+data = Data()
+data.add_df(df)
+```
+
+!!! note
+    If you want to work with `pyspark` DataFrame and `ipyvizzu`, you need to
+    install `pyspark` or install it as an extra:
+
+    ```sh
+    pip install ipyvizzu[pyspark]
+    ```
+
 ### Using `numpy` Array
 
 Use
diff --git a/package-lock.json b/package-lock.json
index d7cab8625..f07906ed0 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -24,16 +24,81 @@
       }
     },
     "node_modules/@babel/code-frame": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.5.tgz",
-      "integrity": "sha512-Xmwn266vad+6DAqEB2A6V/CcZVp62BbwVmcOJc2RPuwih1kw02TjQvWVWlcKGbBPd+8/0V5DEkOcizRGYsspYQ==",
+      "version": "7.22.10",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.22.10.tgz",
+      "integrity": "sha512-/KKIMG4UEL35WmI9OlvMhurwtytjvXoFcGNrOvyG9zIzA8YmPjVtIZUf7b05+TPO7G7/GEmLHDaoCgACHl9hhA==",
       "dependencies": {
-        "@babel/highlight": "^7.22.5"
+        "@babel/highlight": "^7.22.10",
+        "chalk": "^2.4.2"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
+    "node_modules/@babel/code-frame/node_modules/ansi-styles": {
+      "version": "3.2.1",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-3.2.1.tgz",
+      "integrity": "sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==",
+      "dependencies": {
+        "color-convert": "^1.9.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/chalk": {
+      "version": "2.4.2",
+      "resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz",
+      "integrity": "sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==",
+      "dependencies": {
+        "ansi-styles": "^3.2.1",
+        "escape-string-regexp": "^1.0.5",
+        "supports-color": "^5.3.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/color-convert": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-1.9.3.tgz",
+      "integrity": "sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==",
+      "dependencies": {
+        "color-name": "1.1.3"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/color-name": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.3.tgz",
+      "integrity": "sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw=="
+    },
+    "node_modules/@babel/code-frame/node_modules/escape-string-regexp": {
+      "version": "1.0.5",
+      "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz",
+      "integrity": "sha512-vbRorB5FUQWvla16U8R/qgaFIya2qGzwDrNmCZuYKrbdSUMG6I1ZCGQRefkRVhuOkIGVne7BQ35DSfo1qvJqFg==",
+      "engines": {
+        "node": ">=0.8.0"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/has-flag": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz",
+      "integrity": "sha512-sKJf1+ceQBr4SMkvQnBDNDtf4TXpVhVGateu0t918bl30FnbE2m4vNLX+VWe/dpjlb+HugGYzW7uQXH98HPEYw==",
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/supports-color": {
+      "version": "5.5.0",
+      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz",
+      "integrity": "sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==",
+      "dependencies": {
+        "has-flag": "^3.0.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
     "node_modules/@babel/helper-validator-identifier": {
       "version": "7.22.5",
       "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.5.tgz",
@@ -43,12 +108,12 @@
       }
     },
     "node_modules/@babel/highlight": {
-      "version": "7.22.5",
-      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.22.5.tgz",
-      "integrity": "sha512-BSKlD1hgnedS5XRnGOljZawtag7H1yPfQp0tdNJCHoH6AZ+Pcm9VvkrK59/Yy593Ypg0zMxH2BxD1VPYUQ7UIw==",
+      "version": "7.22.10",
+      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.22.10.tgz",
+      "integrity": "sha512-78aUtVcT7MUscr0K5mIEnkwxPE0MaxkR5RxRwuHaQ+JuU5AmTPhY+do2mdzVTnIJJpyBglql2pehuBIWHug+WQ==",
       "dependencies": {
         "@babel/helper-validator-identifier": "^7.22.5",
-        "chalk": "^2.0.0",
+        "chalk": "^2.4.2",
         "js-tokens": "^4.0.0"
       },
       "engines": {
@@ -142,9 +207,9 @@
       }
     },
     "node_modules/@eslint/eslintrc": {
-      "version": "2.1.0",
-      "resolved": "https://registry.npmjs.org/@eslint/eslintrc/-/eslintrc-2.1.0.tgz",
-      "integrity": "sha512-Lj7DECXqIVCqnqjjHMPna4vn6GJcMgul/wuS0je9OZ9gsL0zzDpKPVtcG1HaDVc+9y+qgXneTeUMbCqXJNpH1A==",
+      "version": "2.1.2",
+      "resolved": "https://registry.npmjs.org/@eslint/eslintrc/-/eslintrc-2.1.2.tgz",
+      "integrity": "sha512-+wvgpDsrB1YqAMdEUCcnTlpfVBH7Vqn6A/NT3D8WVXFIaKMlErPIZT3oCIAVCOtarRpMtelZLqJeU3t7WY6X6g==",
       "dependencies": {
         "ajv": "^6.12.4",
         "debug": "^4.3.2",
@@ -164,9 +229,9 @@
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     },
     "node_modules/@eslint/js": {
-      "version": "8.44.0",
-      "resolved": "https://registry.npmjs.org/@eslint/js/-/js-8.44.0.tgz",
-      "integrity": "sha512-Ag+9YM4ocKQx9AarydN0KY2j0ErMHNIocPDrVo8zAE44xLTjEtz81OdR68/cydGtk6m6jDb5Za3r2useMzYmSw==",
+      "version": "8.47.0",
+      "resolved": "https://registry.npmjs.org/@eslint/js/-/js-8.47.0.tgz",
+      "integrity": "sha512-P6omY1zv5MItm93kLM8s2vr1HICJH8v0dvddDhysbIuZ+vcjOHg5Zbkf1mTkcmi2JA9oBG2anOkRnW8WJTS8Og==",
       "engines": {
         "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
       }
@@ -269,9 +334,9 @@
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     },
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-      "version": "20.4.5",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.4.5.tgz",
-      "integrity": "sha512-rt40Nk13II9JwQBdeYqmbn2Q6IVTA5uPhvSO+JVqdXw/6/4glI6oR9ezty/A9Hg5u7JH4OmYmuQ+XvjKm0Datg==",
+      "version": "20.5.0",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.5.0.tgz",
+      "integrity": "sha512-Mgq7eCtoTjT89FqNoTzzXg2XvCi5VMhRV6+I2aYanc6kQCBImeNaAYRs/DyoVqk1YEUJK5gN9VO7HRIdz4Wo3Q==",
       "optional": true
     },
     "node_modules/@types/yauzl": {
@@ -386,6 +451,25 @@
         "url": "https://github.com/sponsors/ljharb"
       }
     },
+    "node_modules/array.prototype.findlastindex": {
+      "version": "1.2.2",
+      "resolved": "https://registry.npmjs.org/array.prototype.findlastindex/-/array.prototype.findlastindex-1.2.2.tgz",
+      "integrity": "sha512-tb5thFFlUcp7NdNF6/MpDk/1r/4awWG1FIz3YqDf+/zJSTezBb+/5WViH41obXULHVpDzoiCLpJ/ZO9YbJMsdw==",
+      "peer": true,
+      "dependencies": {
+        "call-bind": "^1.0.2",
+        "define-properties": "^1.1.4",
+        "es-abstract": "^1.20.4",
+        "es-shim-unscopables": "^1.0.0",
+        "get-intrinsic": "^1.1.3"
+      },
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
     "node_modules/array.prototype.flat": {
       "version": "1.3.1",
       "resolved": "https://registry.npmjs.org/array.prototype.flat/-/array.prototype.flat-1.3.1.tgz",
@@ -856,26 +940,26 @@
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     "node_modules/eslint": {
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-      "resolved": "https://registry.npmjs.org/eslint/-/eslint-8.45.0.tgz",
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+      "version": "8.47.0",
+      "resolved": "https://registry.npmjs.org/eslint/-/eslint-8.47.0.tgz",
+      "integrity": "sha512-spUQWrdPt+pRVP1TTJLmfRNJJHHZryFmptzcafwSvHsceV81djHOdnEeDmkdotZyLNjDhrOasNK8nikkoG1O8Q==",
       "dependencies": {
         "@eslint-community/eslint-utils": "^4.2.0",
-        "@eslint-community/regexpp": "^4.4.0",
-        "@eslint/eslintrc": "^2.1.0",
-        "@eslint/js": "8.44.0",
+        "@eslint-community/regexpp": "^4.6.1",
+        "@eslint/eslintrc": "^2.1.2",
+        "@eslint/js": "^8.47.0",
         "@humanwhocodes/config-array": "^0.11.10",
         "@humanwhocodes/module-importer": "^1.0.1",
         "@nodelib/fs.walk": "^1.2.8",
-        "ajv": "^6.10.0",
+        "ajv": "^6.12.4",
         "chalk": "^4.0.0",
         "cross-spawn": "^7.0.2",
         "debug": "^4.3.2",
         "doctrine": "^3.0.0",
         "escape-string-regexp": "^4.0.0",
-        "eslint-scope": "^7.2.0",
-        "eslint-visitor-keys": "^3.4.1",
-        "espree": "^9.6.0",
+        "eslint-scope": "^7.2.2",
+        "eslint-visitor-keys": "^3.4.3",
+        "espree": "^9.6.1",
         "esquery": "^1.4.2",
         "esutils": "^2.0.2",
         "fast-deep-equal": "^3.1.3",
@@ -909,9 +993,9 @@
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     },
     "node_modules/eslint-config-prettier": {
-      "version": "8.8.0",
-      "resolved": "https://registry.npmjs.org/eslint-config-prettier/-/eslint-config-prettier-8.8.0.tgz",
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+      "version": "9.0.0",
+      "resolved": "https://registry.npmjs.org/eslint-config-prettier/-/eslint-config-prettier-9.0.0.tgz",
+      "integrity": "sha512-IcJsTkJae2S35pRsRAwoCE+925rJJStOdkKnLVgtE+tEpqU0EVVM7OqrwxqgptKdX29NUwC82I5pXsGFIgSevw==",
       "bin": {
         "eslint-config-prettier": "bin/cli.js"
       },
@@ -948,14 +1032,14 @@
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     "node_modules/eslint-import-resolver-node": {
-      "version": "0.3.7",
-      "resolved": "https://registry.npmjs.org/eslint-import-resolver-node/-/eslint-import-resolver-node-0.3.7.tgz",
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+      "version": "0.3.9",
+      "resolved": "https://registry.npmjs.org/eslint-import-resolver-node/-/eslint-import-resolver-node-0.3.9.tgz",
+      "integrity": "sha512-WFj2isz22JahUv+B788TlO3N6zL3nNJGU8CcZbPZvVEkBPaJdCV4vy5wyghty5ROFbCRnm132v8BScu5/1BQ8g==",
       "peer": true,
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         "debug": "^3.2.7",
-        "is-core-module": "^2.11.0",
-        "resolve": "^1.22.1"
+        "is-core-module": "^2.13.0",
+        "resolve": "^1.22.4"
       }
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     "node_modules/eslint-import-resolver-node/node_modules/debug": {
@@ -1013,26 +1097,29 @@
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-      "resolved": "https://registry.npmjs.org/eslint-plugin-import/-/eslint-plugin-import-2.27.5.tgz",
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+      "version": "2.28.0",
+      "resolved": "https://registry.npmjs.org/eslint-plugin-import/-/eslint-plugin-import-2.28.0.tgz",
+      "integrity": "sha512-B8s/n+ZluN7sxj9eUf7/pRFERX0r5bnFA2dCaLHy2ZeaQEAz0k+ZZkFWRFHJAqxfxQDx6KLv9LeIki7cFdwW+Q==",
       "peer": true,
       "dependencies": {
         "array-includes": "^3.1.6",
+        "array.prototype.findlastindex": "^1.2.2",
         "array.prototype.flat": "^1.3.1",
         "array.prototype.flatmap": "^1.3.1",
         "debug": "^3.2.7",
         "doctrine": "^2.1.0",
         "eslint-import-resolver-node": "^0.3.7",
-        "eslint-module-utils": "^2.7.4",
+        "eslint-module-utils": "^2.8.0",
         "has": "^1.0.3",
-        "is-core-module": "^2.11.0",
+        "is-core-module": "^2.12.1",
         "is-glob": "^4.0.3",
         "minimatch": "^3.1.2",
+        "object.fromentries": "^2.0.6",
+        "object.groupby": "^1.0.0",
         "object.values": "^1.1.6",
-        "resolve": "^1.22.1",
-        "semver": "^6.3.0",
-        "tsconfig-paths": "^3.14.1"
+        "resolve": "^1.22.3",
+        "semver": "^6.3.1",
+        "tsconfig-paths": "^3.14.2"
       },
       "engines": {
         "node": ">=4"
@@ -1115,9 +1202,9 @@
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-      "resolved": "https://registry.npmjs.org/eslint-scope/-/eslint-scope-7.2.1.tgz",
-      "integrity": "sha512-CvefSOsDdaYYvxChovdrPo/ZGt8d5lrJWleAc1diXRKhHGiTYEI26cvo8Kle/wGnsizoCJjK73FMg1/IkIwiNA==",
+      "version": "7.2.2",
+      "resolved": "https://registry.npmjs.org/eslint-scope/-/eslint-scope-7.2.2.tgz",
+      "integrity": "sha512-dOt21O7lTMhDM+X9mB4GX+DZrZtCUJPL/wlcTqxyrx5IvO0IYtILdtrQGQp+8n5S0gwSVmOf9NQrjMOgfQZlIg==",
       "dependencies": {
         "esrecurse": "^4.3.0",
         "estraverse": "^5.2.0"
@@ -1130,9 +1217,9 @@
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     "node_modules/eslint-visitor-keys": {
-      "version": "3.4.1",
-      "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-3.4.1.tgz",
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+      "version": "3.4.3",
+      "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-3.4.3.tgz",
+      "integrity": "sha512-wpc+LXeiyiisxPlEkUzU6svyS1frIO3Mgxj1fdy7Pm8Ygzguax2N3Fa/D/ag1WqbOprdI+uY6wMUl8/a2G+iag==",
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         "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
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@@ -1417,9 +1504,9 @@
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-      "resolved": "https://registry.npmjs.org/globals/-/globals-13.20.0.tgz",
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+      "resolved": "https://registry.npmjs.org/globals/-/globals-13.21.0.tgz",
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@@ -1689,9 +1776,9 @@
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-      "resolved": "https://registry.npmjs.org/is-core-module/-/is-core-module-2.12.1.tgz",
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+      "version": "2.13.0",
+      "resolved": "https://registry.npmjs.org/is-core-module/-/is-core-module-2.13.0.tgz",
+      "integrity": "sha512-Z7dk6Qo8pOCp3l4tsX2C5ZVas4V+UxwQodwZhLopL91TX8UyyHEXafPcyoeeWuLrwzHcr3igO78wNLwHJHsMCQ==",
       "dependencies": {
         "has": "^1.0.3"
       },
@@ -2257,6 +2344,35 @@
         "url": "https://github.com/sponsors/ljharb"
       }
     },
+    "node_modules/object.fromentries": {
+      "version": "2.0.6",
+      "resolved": "https://registry.npmjs.org/object.fromentries/-/object.fromentries-2.0.6.tgz",
+      "integrity": "sha512-VciD13dswC4j1Xt5394WR4MzmAQmlgN72phd/riNp9vtD7tp4QQWJ0R4wvclXcafgcYK8veHRed2W6XeGBvcfg==",
+      "peer": true,
+      "dependencies": {
+        "call-bind": "^1.0.2",
+        "define-properties": "^1.1.4",
+        "es-abstract": "^1.20.4"
+      },
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
+    "node_modules/object.groupby": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/object.groupby/-/object.groupby-1.0.0.tgz",
+      "integrity": "sha512-70MWG6NfRH9GnbZOikuhPPYzpUpof9iW2J9E4dW7FXTqPNb6rllE6u39SKwwiNh8lCwX3DDb5OgcKGiEBrTTyw==",
+      "peer": true,
+      "dependencies": {
+        "call-bind": "^1.0.2",
+        "define-properties": "^1.2.0",
+        "es-abstract": "^1.21.2",
+        "get-intrinsic": "^1.2.1"
+      }
+    },
     "node_modules/object.values": {
       "version": "1.1.6",
       "resolved": "https://registry.npmjs.org/object.values/-/object.values-1.1.6.tgz",
@@ -2424,9 +2540,9 @@
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-      "version": "3.0.0",
-      "resolved": "https://registry.npmjs.org/prettier/-/prettier-3.0.0.tgz",
-      "integrity": "sha512-zBf5eHpwHOGPC47h0zrPyNn+eAEIdEzfywMoYn2XPi0P44Zp0tSq64rq0xAREh4auw2cJZHo9QUob+NqCQky4g==",
+      "version": "3.0.1",
+      "resolved": "https://registry.npmjs.org/prettier/-/prettier-3.0.1.tgz",
+      "integrity": "sha512-fcOWSnnpCrovBsmFZIGIy9UqK2FaI7Hqax+DIO0A9UxeVoY4iweyaFjS5TavZN97Hfehph0nhsZnjlVKzEQSrQ==",
       "bin": {
         "prettier": "bin/prettier.cjs"
       },
@@ -2591,11 +2707,11 @@
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-      "resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.2.tgz",
-      "integrity": "sha512-Sb+mjNHOULsBv818T40qSPeRiuWLyaGMa5ewydRLFimneixmVy2zdivRl+AF6jaYPC8ERxGDmFSiqui6SfPd+g==",
+      "version": "1.22.4",
+      "resolved": "https://registry.npmjs.org/resolve/-/resolve-1.22.4.tgz",
+      "integrity": "sha512-PXNdCiPqDqeUou+w1C2eTQbNfxKSuMxqTCuvlmmMsk1NWHL5fRrhY6Pl0qEYYc6+QqGClco1Qj8XnjPego4wfg==",
       "dependencies": {
-        "is-core-module": "^2.11.0",
+        "is-core-module": "^2.13.0",
         "path-parse": "^1.0.7",
         "supports-preserve-symlinks-flag": "^1.0.0"
       },
diff --git a/pdm.lock b/pdm.lock
index f2d27d45f..66ef7e9dd 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -6,7 +6,7 @@ groups = ["codequality", "docs", "env", "packagedeps", "packagetool"]
 cross_platform = true
 static_urls = false
 lock_version = "4.3"
-content_hash = "sha256:43c5f7462c6a1e863dd6f0e35048919c3ea1e513fb3eaf08c2e6cfdf4a97e4b8"
+content_hash = "sha256:26035202af4ceb9787b7afb55a6fa887249cce90ab8242c647b2e3e2e495508d"
 
 [[package]]
 name = "adagio"
@@ -421,12 +421,12 @@ files = [
 
 [[package]]
 name = "cfgv"
-version = "3.3.1"
-requires_python = ">=3.6.1"
+version = "3.4.0"
+requires_python = ">=3.8"
 summary = "Validate configuration and produce human readable error messages."
 files = [
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+    {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"},
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-version = "1.6.7"
+version = "1.6.7.post1"
 requires_python = ">=3.7"
 summary = "An implementation of the Debug Adapter Protocol for Python"
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 ]
 
 [[package]]
@@ -692,12 +692,12 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.1.2"
+version = "1.1.3"
 requires_python = ">=3.7"
 summary = "Backport of PEP 654 (exception groups)"
 files = [
-    {file = "exceptiongroup-1.1.2-py3-none-any.whl", hash = "sha256:e346e69d186172ca7cf029c8c1d16235aa0e04035e5750b4b95039e65204328f"},
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+    {file = "exceptiongroup-1.1.3-py3-none-any.whl", hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3"},
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 ]
 
 [[package]]
@@ -893,7 +893,7 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.25.0"
+version = "6.25.1"
 requires_python = ">=3.8"
 summary = "IPython Kernel for Jupyter"
 dependencies = [
@@ -912,8 +912,8 @@ dependencies = [
     "traitlets>=5.4.0",
 ]
 files = [
-    {file = "ipykernel-6.25.0-py3-none-any.whl", hash = "sha256:f0042e867ac3f6bca1679e6a88cbd6a58ed93a44f9d0866aecde6efe8de76659"},
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+    {file = "ipykernel-6.25.1-py3-none-any.whl", hash = "sha256:c8a2430b357073b37c76c21c52184db42f6b4b0e438e1eb7df3c4440d120497c"},
+    {file = "ipykernel-6.25.1.tar.gz", hash = "sha256:050391364c0977e768e354bdb60cbbfbee7cbb943b1af1618382021136ffd42f"},
 ]
 
 [[package]]
@@ -1045,7 +1045,7 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.18.6"
+version = "4.19.0"
 requires_python = ">=3.8"
 summary = "An implementation of JSON Schema validation for Python"
 dependencies = [
@@ -1055,8 +1055,8 @@ dependencies = [
     "rpds-py>=0.7.1",
 ]
 files = [
-    {file = "jsonschema-4.18.6-py3-none-any.whl", hash = "sha256:dc274409c36175aad949c68e5ead0853aaffbe8e88c830ae66bb3c7a1728ad2d"},
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+    {file = "jsonschema-4.19.0.tar.gz", hash = "sha256:6e1e7569ac13be8139b2dd2c21a55d350066ee3f80df06c608b398cdc6f30e8f"},
 ]
 
 [[package]]
@@ -1074,7 +1074,7 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.18.6"
+version = "4.19.0"
 extras = ["format-nongpl"]
 requires_python = ">=3.8"
 summary = "An implementation of JSON Schema validation for Python"
@@ -1083,15 +1083,15 @@ dependencies = [
     "idna",
     "isoduration",
     "jsonpointer>1.13",
-    "jsonschema==4.18.6",
+    "jsonschema==4.19.0",
     "rfc3339-validator",
     "rfc3986-validator>0.1.0",
     "uri-template",
     "webcolors>=1.11",
 ]
 files = [
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 ]
 
 [[package]]
@@ -1205,7 +1205,7 @@ files = [
 
 [[package]]
 name = "jupyterlab"
-version = "4.0.4"
+version = "4.0.5"
 requires_python = ">=3.8"
 summary = "JupyterLab computational environment"
 dependencies = [
@@ -1223,8 +1223,8 @@ dependencies = [
     "traitlets",
 ]
 files = [
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 ]
 
 [[package]]
@@ -1891,16 +1891,16 @@ files = [
 
 [[package]]
 name = "mkdocstrings-python"
-version = "1.2.1"
+version = "1.3.0"
 requires_python = ">=3.8"
 summary = "A Python handler for mkdocstrings."
 dependencies = [
-    "griffe>=0.30",
+    "griffe<0.33,>=0.30",
     "mkdocstrings>=0.20",
 ]
 files = [
-    {file = "mkdocstrings_python-1.2.1-py3-none-any.whl", hash = "sha256:7c08f33e9ba7b1655e9cf0608eba3ce7a9513bd8b42a68a8d24ffaf4a6a50cfc"},
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+    {file = "mkdocstrings_python-1.3.0.tar.gz", hash = "sha256:f967f84bab530fcc13cc9c02eccf0c18bdb2c3bab5c55fa2045938681eec4fc4"},
 ]
 
 [[package]]
@@ -2000,8 +2000,8 @@ files = [
 
 [[package]]
 name = "mypy"
-version = "1.4.1"
-requires_python = ">=3.7"
+version = "1.5.0"
+requires_python = ">=3.8"
 summary = "Optional static typing for Python"
 dependencies = [
     "mypy-extensions>=1.0.0",
@@ -2009,32 +2009,28 @@ dependencies = [
     "typing-extensions>=4.1.0",
 ]
 files = [
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 name = "types-pytz"
-version = "2023.3.0.0"
+version = "2023.3.0.1"
 summary = "Typing stubs for pytz"
 files = [
-    {file = "types-pytz-2023.3.0.0.tar.gz", hash = "sha256:ecdc70d543aaf3616a7e48631543a884f74205f284cefd6649ddf44c6a820aac"},
-    {file = "types_pytz-2023.3.0.0-py3-none-any.whl", hash = "sha256:4fc2a7fbbc315f0b6630e0b899fd6c743705abe1094d007b0e612d10da15e0f3"},
+    {file = "types-pytz-2023.3.0.1.tar.gz", hash = "sha256:1a7b8d4aac70981cfa24478a41eadfcd96a087c986d6f150d77e3ceb3c2bdfab"},
+    {file = "types_pytz-2023.3.0.1-py3-none-any.whl", hash = "sha256:65152e872137926bb67a8fe6cc9cfd794365df86650c5d5fdc7b167b0f38892e"},
 ]
 
 [[package]]
@@ -3478,7 +3511,7 @@ files = [
 
 [[package]]
 name = "virtualenv"
-version = "20.24.2"
+version = "20.24.3"
 requires_python = ">=3.7"
 summary = "Virtual Python Environment builder"
 dependencies = [
@@ -3487,8 +3520,8 @@ dependencies = [
     "platformdirs<4,>=3.9.1",
 ]
 files = [
-    {file = "virtualenv-20.24.2-py3-none-any.whl", hash = "sha256:43a3052be36080548bdee0b42919c88072037d50d56c28bd3f853cbe92b953ff"},
-    {file = "virtualenv-20.24.2.tar.gz", hash = "sha256:fd8a78f46f6b99a67b7ec5cf73f92357891a7b3a40fd97637c27f854aae3b9e0"},
+    {file = "virtualenv-20.24.3-py3-none-any.whl", hash = "sha256:95a6e9398b4967fbcb5fef2acec5efaf9aa4972049d9ae41f95e0972a683fd02"},
+    {file = "virtualenv-20.24.3.tar.gz", hash = "sha256:e5c3b4ce817b0b328af041506a2a299418c98747c4b1e68cb7527e74ced23efc"},
 ]
 
 [[package]]
diff --git a/pyproject.toml b/pyproject.toml
index 1f1929aec..21da0d340 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,6 +8,7 @@ authors = [
 readme = "README.md"
 requires-python = ">=3.6"
 dependencies = [
+    "typing_extensions ; python_version < '3.8'",
     "IPython",
     "jsonschema",
 ]
@@ -40,6 +41,9 @@ keywords = [
 pandas = [
     "pandas",
 ]
+pyspark = [
+    "pyspark",
+]
 numpy = [
     "numpy",
 ]
@@ -149,6 +153,7 @@ deps =
     coverage[toml]
 extras =
     pandas
+    pyspark
     numpy
 commands =
     python -m coverage run --branch --source ipyvizzu -m unittest discover tests
@@ -182,6 +187,7 @@ packagedeps = [
     "jsonschema ; python_version >= '3.10'",
     "pandas ; python_version >= '3.10'",
     "pandas-stubs ; python_version >= '3.10'",
+    "pyspark ; python_version >= '3.10'",
     "numpy ; python_version >= '3.10'",
     "fugue >= 0.8.1 ; python_version >= '3.10'",
 ]
@@ -241,4 +247,4 @@ disable = ["fixme"]
 good-names= ["i", "df"]
 
 [tool.coverage.paths]
-source = ["src/", ".tox/py*/lib/python*/site-packages/"]
+source = ["src/", ".tox/py*/lib/python*/site-packages/"]
\ No newline at end of file
diff --git a/src/ipyvizzu/__init__.py b/src/ipyvizzu/__init__.py
index 020ce0aa7..4b66b83f0 100644
--- a/src/ipyvizzu/__init__.py
+++ b/src/ipyvizzu/__init__.py
@@ -46,7 +46,6 @@
 * [DisplayTemplate][ipyvizzu.template.DisplayTemplate]
 """
 
-import sys
 import warnings
 
 from .chart import Chart
@@ -70,7 +69,7 @@
 from .template import ChartProperty, DisplayTarget, DisplayTemplate
 from .event import EventHandler
 
-from .__version__ import __version__
+from .__version__ import __version__, PYENV
 
 __all__ = [
     "Chart",
@@ -103,10 +102,8 @@
 ]
 
 
-if sys.version_info >= (3, 7):
-    pass
-else:
-    # TODO: remove once support for Python 3.6 is dropped
+# TODO: remove once support for Python 3.6 is dropped
+if PYENV < (3, 7):
     warnings.warn(
         "Python 3.6 support will be dropped in future versions.",
         FutureWarning,
diff --git a/src/ipyvizzu/__version__.py b/src/ipyvizzu/__version__.py
index 8335749b7..752c831f5 100644
--- a/src/ipyvizzu/__version__.py
+++ b/src/ipyvizzu/__version__.py
@@ -1,3 +1,8 @@
 """A module for storing version number."""
 
+import sys
+
+
 __version__ = "0.15.0"
+
+PYENV = sys.version_info
diff --git a/src/ipyvizzu/animation.py b/src/ipyvizzu/animation.py
index 921918eca..8e2fd935b 100644
--- a/src/ipyvizzu/animation.py
+++ b/src/ipyvizzu/animation.py
@@ -3,14 +3,16 @@
 import abc
 import json
 from os import PathLike
-from typing import List, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Type, Union
 import warnings
 
 import jsonschema  # type: ignore
 
-from ipyvizzu.data.converters.pandas.converter import PandasDataFrameConverter
-from ipyvizzu.data.converters.numpy.converter import NumpyArrayConverter
-from ipyvizzu.data.converters.numpy.type_alias import ColumnName, ColumnDtype
+from ipyvizzu.data.converters.defaults import NAN_DIMENSION, NAN_MEASURE
+from ipyvizzu.data.converters.df.defaults import MAX_ROWS
+from ipyvizzu.data.converters.numpy import ColumnDtype, ColumnName, NumpyArrayConverter
+from ipyvizzu.data.converters.pandas import PandasDataFrameConverter
+from ipyvizzu.data.converters.spark import SparkDataFrame, SparkDataFrameConverter
 from ipyvizzu.data.type_alias import (
     DimensionValue,
     NestedMeasureValues,
@@ -274,24 +276,35 @@ def add_measure(
 
     def add_df(
         self,
-        df: Optional[Union["pd.DataFrame", "pd.Series"]],  # type: ignore
-        default_measure_value: Optional[MeasureValue] = 0,
-        default_dimension_value: Optional[DimensionValue] = "",
+        df: Optional[  # type: ignore
+            Union[
+                "pandas.DataFrame",
+                "pandas.Series",
+                "pyspark.sql.DataFrame",
+            ]
+        ],
+        default_measure_value: MeasureValue = NAN_MEASURE,
+        default_dimension_value: DimensionValue = NAN_DIMENSION,
+        max_rows: int = MAX_ROWS,
         include_index: Optional[str] = None,
     ) -> None:
         """
-        Add a `pandas` `DataFrame` or `Series` to an existing
-        [Data][ipyvizzu.animation.Data] class instance.
+        Add a `pandas` `DataFrame`, `Series` or a `pyspark` `DataFrame`
+        to an existing [Data][ipyvizzu.animation.Data] class instance.
 
         Args:
             df:
-                The `pandas` `DataFrame` or `Series` to add.
+                The `pandas` `DataFrame`, `Series` or the `pyspark` `DataFrame`to add.
             default_measure_value:
                 The default measure value to fill empty values. Defaults to 0.
             default_dimension_value:
                 The default dimension value to fill empty values. Defaults to an empty string.
+            max_rows: The maximum number of rows to include in the converted series list.
+                If the `df` contains more rows,
+                a random sample of the given number of rows (approximately) will be taken.
             include_index:
                 Add the data frame's index as a column with the given name. Defaults to `None`.
+                (Cannot be used with `pyspark` `DataFrame`.)
 
         Example:
             Adding a data frame to a [Data][ipyvizzu.animation.Data] class instance:
@@ -307,17 +320,36 @@ def add_df(
                 data.add_df(df)
         """
 
-        converter = PandasDataFrameConverter(
-            df, default_measure_value, default_dimension_value, include_index
-        )
-        series_list = converter.get_series_list()
-        self.add_series_list(series_list)
+        # pylint: disable=too-many-arguments
+
+        if not isinstance(df, type(None)):
+            arguments = {
+                "df": df,
+                "default_measure_value": default_measure_value,
+                "default_dimension_value": default_dimension_value,
+                "max_rows": max_rows,
+                "include_index": include_index,
+            }
+            Converter: Union[
+                Type[PandasDataFrameConverter], Type[SparkDataFrameConverter]
+            ] = PandasDataFrameConverter
+            if isinstance(df, SparkDataFrame):
+                Converter = SparkDataFrameConverter
+                if arguments["include_index"] is not None:
+                    raise ValueError(
+                        "`include_index` cannot be used with `pyspark` `DataFrame`"
+                    )
+                del arguments["include_index"]
+
+            converter = Converter(**arguments)  # type: ignore
+            series_list = converter.get_series_list()
+            self.add_series_list(series_list)
 
     def add_data_frame(
         self,
-        data_frame: Optional[Union["pd.DataFrame", "pd.Series"]],  # type: ignore
-        default_measure_value: Optional[MeasureValue] = 0,
-        default_dimension_value: Optional[DimensionValue] = "",
+        data_frame: Optional[Union["pandas.DataFrame", "pandas.Series"]],  # type: ignore
+        default_measure_value: MeasureValue = NAN_MEASURE,
+        default_dimension_value: DimensionValue = NAN_DIMENSION,
     ) -> None:
         """
         [Deprecated] This function is deprecated and will be removed in future versions.
@@ -349,7 +381,7 @@ def add_data_frame(
 
     def add_df_index(
         self,
-        df: Optional[Union["pd.DataFrame", "pd.Series"]],  # type: ignore
+        df: Optional[Union["pandas.DataFrame", "pandas.Series"]],  # type: ignore
         column_name: str = "Index",
     ) -> None:
         """
@@ -375,14 +407,14 @@ def add_df_index(
                 data.add_df(df)
         """
 
-        converter = PandasDataFrameConverter(df, include_index=column_name)
-        index_series = converter.get_series_from_index()
-        if index_series:
-            self.add_series(**index_series)  # type: ignore
+        if not isinstance(df, type(None)):
+            converter = PandasDataFrameConverter(df, include_index=column_name)
+            series_list = converter.get_series_from_index()
+            self.add_series_list(series_list)
 
     def add_data_frame_index(
         self,
-        data_frame: Optional[Union["pd.DataFrame", "pd.Series"]],  # type: ignore
+        data_frame: Optional[Union["pandas.DataFrame", "pandas.Series"]],  # type: ignore
         name: str,
     ) -> None:
         """
@@ -411,11 +443,11 @@ def add_data_frame_index(
 
     def add_np_array(
         self,
-        np_array: Optional["np.array"],  # type: ignore
+        np_array: Optional["numpy.array"],  # type: ignore
         column_name: Optional[ColumnName] = None,
         column_dtype: Optional[ColumnDtype] = None,
-        default_measure_value: Optional[MeasureValue] = 0,
-        default_dimension_value: Optional[DimensionValue] = "",
+        default_measure_value: MeasureValue = NAN_MEASURE,
+        default_dimension_value: DimensionValue = NAN_DIMENSION,
     ) -> None:
         """
         Add a `numpy` `array` to an existing
@@ -444,15 +476,16 @@ def add_np_array(
 
         # pylint: disable=too-many-arguments
 
-        converter = NumpyArrayConverter(
-            np_array,
-            column_name,
-            column_dtype,
-            default_measure_value,
-            default_dimension_value,
-        )
-        series_list = converter.get_series_list()
-        self.add_series_list(series_list)
+        if not isinstance(np_array, type(None)):
+            converter = NumpyArrayConverter(
+                np_array,
+                column_name,
+                column_dtype,
+                default_measure_value,
+                default_dimension_value,
+            )
+            series_list = converter.get_series_list()
+            self.add_series_list(series_list)
 
     def _add_named_value(
         self,
diff --git a/src/ipyvizzu/data/converters/converter.py b/src/ipyvizzu/data/converters/converter.py
index d22decca3..2eb752e75 100644
--- a/src/ipyvizzu/data/converters/converter.py
+++ b/src/ipyvizzu/data/converters/converter.py
@@ -22,6 +22,14 @@ class ToSeriesListConverter(ABC):
 
     # pylint: disable=too-few-public-methods
 
+    def __init__(
+        self,
+        default_measure_value: MeasureValue,
+        default_dimension_value: DimensionValue,
+    ) -> None:
+        self._default_measure_value = default_measure_value
+        self._default_dimension_value = default_dimension_value
+
     @abstractmethod
     def get_series_list(self) -> List[Series]:
         """
diff --git a/src/ipyvizzu/data/converters/defaults.py b/src/ipyvizzu/data/converters/defaults.py
new file mode 100644
index 000000000..3e9d3b763
--- /dev/null
+++ b/src/ipyvizzu/data/converters/defaults.py
@@ -0,0 +1,12 @@
+"""
+This module provides default values for converters.
+"""
+
+from ipyvizzu.data.type_alias import DimensionValue, MeasureValue
+
+
+NAN_DIMENSION: DimensionValue = ""
+"""Default dimension value to replace nan values."""
+
+NAN_MEASURE: MeasureValue = 0
+"""Default measure value to replace nan values."""
diff --git a/src/ipyvizzu/data/converters/df/__init__.py b/src/ipyvizzu/data/converters/df/__init__.py
new file mode 100644
index 000000000..c6e70095e
--- /dev/null
+++ b/src/ipyvizzu/data/converters/df/__init__.py
@@ -0,0 +1,3 @@
+"""
+This module provides modules for data frame converter.
+"""
diff --git a/src/ipyvizzu/data/converters/df/converter.py b/src/ipyvizzu/data/converters/df/converter.py
new file mode 100644
index 000000000..0579911a0
--- /dev/null
+++ b/src/ipyvizzu/data/converters/df/converter.py
@@ -0,0 +1,62 @@
+"""
+This module provides the `DataFrameConverter` abstract class.
+"""
+
+from abc import abstractmethod
+from typing import List
+
+from ipyvizzu.data.converters.converter import ToSeriesListConverter
+from ipyvizzu.data.converters.df.type_alias import DataFrame
+from ipyvizzu.data.type_alias import (
+    DimensionValue,
+    MeasureValue,
+    Series,
+)
+
+
+class DataFrameConverter(ToSeriesListConverter):
+    """
+    Converts data frame into a list of dictionaries representing series.
+    Each dictionary contains information about the series `name`, `values` and `type`.
+    """
+
+    # pylint: disable=too-few-public-methods
+
+    def __init__(
+        self,
+        default_measure_value: MeasureValue,
+        default_dimension_value: DimensionValue,
+        max_rows: int,
+    ) -> None:
+        super().__init__(default_measure_value, default_dimension_value)
+        self._max_rows = max_rows
+
+    def get_series_list(self) -> List[Series]:
+        """
+        Convert the `DataFrame` columns to a list of dictionaries representing series.
+
+        Returns:
+            A list of dictionaries representing series,
+            where each dictionary has `name`, `values` and `type` keys.
+        """
+
+        series_list = []
+        for name in self._get_columns():
+            series_list.append(self._get_series_from_column(name))
+        return series_list
+
+    def _get_series_from_column(self, column_name: str) -> Series:
+        values, infer_type = self._convert_to_series_values_and_type(column_name)
+        return self._convert_to_series(column_name, values, infer_type)
+
+    @abstractmethod
+    def _get_sampled_df(self, df: DataFrame) -> DataFrame:
+        """
+        Returns a sampled data frame for the maximum number of rows.
+        """
+
+    @abstractmethod
+    def _get_columns(self) -> List[str]:
+        """
+        Return column names of the data frame.
+        """
diff --git a/src/ipyvizzu/data/converters/df/defaults.py b/src/ipyvizzu/data/converters/df/defaults.py
new file mode 100644
index 000000000..2a898713b
--- /dev/null
+++ b/src/ipyvizzu/data/converters/df/defaults.py
@@ -0,0 +1,6 @@
+"""
+This module provides default values for data frame converter.
+"""
+
+MAX_ROWS: int = 10000
+"""Default maximum number of rows."""
diff --git a/src/ipyvizzu/data/converters/df/type_alias.py b/src/ipyvizzu/data/converters/df/type_alias.py
new file mode 100644
index 000000000..83fb6af05
--- /dev/null
+++ b/src/ipyvizzu/data/converters/df/type_alias.py
@@ -0,0 +1,11 @@
+"""
+This module provides typing aliases for data frame converter.
+"""
+
+from typing import Any, TypeVar
+
+
+DataFrame = TypeVar("DataFrame", Any, Any)
+"""
+Represents a data frame.
+"""
diff --git a/src/ipyvizzu/data/converters/numpy/__init__.py b/src/ipyvizzu/data/converters/numpy/__init__.py
index f0f7f2d25..f3bce6282 100644
--- a/src/ipyvizzu/data/converters/numpy/__init__.py
+++ b/src/ipyvizzu/data/converters/numpy/__init__.py
@@ -1,3 +1,13 @@
 """
 This module provides modules for numpy converter.
 """
+
+from ipyvizzu.data.converters.numpy.converter import NumpyArrayConverter
+from ipyvizzu.data.converters.numpy.type_alias import (
+    Index,
+    Name,
+    DType,
+    ColumnName,
+    ColumnDtype,
+    ColumnConfig,
+)
diff --git a/src/ipyvizzu/data/converters/numpy/converter.py b/src/ipyvizzu/data/converters/numpy/converter.py
index e354a3326..11c20532c 100644
--- a/src/ipyvizzu/data/converters/numpy/converter.py
+++ b/src/ipyvizzu/data/converters/numpy/converter.py
@@ -7,6 +7,7 @@
 from types import ModuleType
 from typing import Dict, List, Optional, Tuple, Union
 
+from ipyvizzu.data.converters.defaults import NAN_DIMENSION, NAN_MEASURE
 from ipyvizzu.data.converters.converter import ToSeriesListConverter
 from ipyvizzu.data.converters.numpy.type_alias import (
     ColumnConfig,
@@ -54,20 +55,19 @@ class NumpyArrayConverter(ToSeriesListConverter):
 
     def __init__(
         self,
-        np_array: Optional["np.array"],  # type: ignore
+        np_array: "numpy.array",  # type: ignore
         column_name: Optional[ColumnName] = None,
         column_dtype: Optional[ColumnDtype] = None,
-        default_measure_value: Optional[MeasureValue] = 0,
-        default_dimension_value: Optional[DimensionValue] = "",
+        default_measure_value: MeasureValue = NAN_MEASURE,
+        default_dimension_value: DimensionValue = NAN_DIMENSION,
     ) -> None:
         # pylint: disable=too-many-arguments
 
+        super().__init__(default_measure_value, default_dimension_value)
         self._np = self._get_numpy()
-        self._np_array = self._get_array(np_array)
+        self._np_array = np_array
         self._column_name: Dict[Index, Name] = self._get_columns_config(column_name)
         self._column_dtype: Dict[Index, DType] = self._get_columns_config(column_dtype)
-        self._default_measure_value = default_measure_value
-        self._default_dimension_value = default_dimension_value
 
     def get_series_list(self) -> List[Series]:
         """
@@ -114,11 +114,6 @@ def _get_numpy(self) -> ModuleType:
                 "numpy is not available. Please install numpy to use this feature."
             ) from error
 
-    def _get_array(self, np_array: Optional["np.array"]) -> "np.array":  # type: ignore
-        if isinstance(np_array, type(None)):
-            return self._np.empty(())
-        return np_array
-
     def _get_columns_config(
         self,
         config: Optional[Union[ColumnConfig, Dict[Index, ColumnConfig]]],
@@ -132,7 +127,7 @@ def _get_columns_config(
         return config
 
     def _convert_to_series_values_and_type(
-        self, obj: Tuple[int, "np.array"]  # type: ignore
+        self, obj: Tuple[int, "numpy.array"]  # type: ignore
     ) -> Tuple[SeriesValues, InferType]:
         column = obj
         i = column[0]
@@ -142,14 +137,18 @@ def _convert_to_series_values_and_type(
             return self._convert_to_measure_values(array), InferType.MEASURE
         return self._convert_to_dimension_values(array), InferType.DIMENSION
 
-    def _convert_to_measure_values(self, obj: "np.array") -> List[MeasureValue]:  # type: ignore
+    def _convert_to_measure_values(
+        self, obj: "numpy.array"  # type: ignore
+    ) -> List[MeasureValue]:
         array = obj
         array_float = array.astype(float)
         return self._np.nan_to_num(
             array_float, nan=self._default_measure_value
         ).tolist()
 
-    def _convert_to_dimension_values(self, obj: "np.array") -> List[DimensionValue]:  # type: ignore
+    def _convert_to_dimension_values(
+        self, obj: "numpy.array"  # type: ignore
+    ) -> List[DimensionValue]:
         array = obj
         array_str = array.astype(str)
         replace_nan = "nan"
diff --git a/src/ipyvizzu/data/converters/pandas/__init__.py b/src/ipyvizzu/data/converters/pandas/__init__.py
index 993059009..c4942ce43 100644
--- a/src/ipyvizzu/data/converters/pandas/__init__.py
+++ b/src/ipyvizzu/data/converters/pandas/__init__.py
@@ -1,3 +1,6 @@
 """
 This module provides modules for pandas converter.
 """
+
+from ipyvizzu.data.converters.pandas.converter import PandasDataFrameConverter
+from ipyvizzu.data.converters.pandas.protocol import PandasDataFrame, PandasSeries
diff --git a/src/ipyvizzu/data/converters/pandas/converter.py b/src/ipyvizzu/data/converters/pandas/converter.py
index 1c8f49dbc..0a06ca154 100644
--- a/src/ipyvizzu/data/converters/pandas/converter.py
+++ b/src/ipyvizzu/data/converters/pandas/converter.py
@@ -7,7 +7,10 @@
 from types import ModuleType
 from typing import List, Optional, Tuple, Union
 
-from ipyvizzu.data.converters.converter import ToSeriesListConverter
+from ipyvizzu.data.converters.defaults import NAN_DIMENSION, NAN_MEASURE
+from ipyvizzu.data.converters.df.defaults import MAX_ROWS
+from ipyvizzu.data.converters.df.converter import DataFrameConverter
+from ipyvizzu.data.converters.pandas.protocol import PandasSeries
 from ipyvizzu.data.infer_type import InferType
 from ipyvizzu.data.type_alias import (
     DimensionValue,
@@ -17,7 +20,7 @@
 )
 
 
-class PandasDataFrameConverter(ToSeriesListConverter):
+class PandasDataFrameConverter(DataFrameConverter):
     """
     Converts a `pandas` `DataFrame` or `Series` into a list of dictionaries representing series.
     Each dictionary contains information about the series `name`, `values` and `type`.
@@ -28,6 +31,9 @@ class PandasDataFrameConverter(ToSeriesListConverter):
             Default value to use for missing measure values. Defaults to 0.
         default_dimension_value:
             Default value to use for missing dimension values. Defaults to an empty string.
+        max_rows: The maximum number of rows to include in the converted series list.
+            If the `df` contains more rows,
+            a random sample of the given number of rows will be taken.
         include_index:
             Name for the index column to include as a series.
             If provided, the index column will be added. Defaults to None.
@@ -41,15 +47,19 @@ class PandasDataFrameConverter(ToSeriesListConverter):
 
     def __init__(
         self,
-        df: Optional[Union["pd.DataFrame", "pd.Series"]],  # type: ignore
-        default_measure_value: Optional[MeasureValue] = 0,
-        default_dimension_value: Optional[DimensionValue] = "",
+        df: Union["pandas.DataFrame", "pandas.Series"],  # type: ignore
+        default_measure_value: MeasureValue = NAN_MEASURE,
+        default_dimension_value: DimensionValue = NAN_DIMENSION,
+        max_rows: int = MAX_ROWS,
         include_index: Optional[str] = None,
     ) -> None:
+        # pylint: disable=too-many-arguments
+
+        super().__init__(default_measure_value, default_dimension_value, max_rows)
         self._pd = self._get_pandas()
-        self._df = self._get_df(df)
-        self._default_measure_value = default_measure_value
-        self._default_dimension_value = default_dimension_value
+        self._df = self._get_sampled_df(
+            self._convert_to_df(df) if isinstance(df, PandasSeries) else df
+        )
         self._include_index = include_index
 
     def get_series_list(self) -> List[Series]:
@@ -61,15 +71,11 @@ def get_series_list(self) -> List[Series]:
             where each dictionary has `name`, `values` and `type` keys.
         """
 
-        series_list = []
+        series_list = super().get_series_list()
         index_series = self.get_series_from_index()
-        if index_series:
-            series_list.append(index_series)
-        for name in self._df.columns:
-            series_list.append(self._get_series_from_column(name))
-        return series_list
+        return index_series + series_list
 
-    def get_series_from_index(self) -> Optional[Series]:
+    def get_series_from_index(self) -> List[Series]:
         """
         Convert the `DataFrame` index to a dictionary representing a series,
         if `include_index` is provided.
@@ -80,15 +86,12 @@ def get_series_from_index(self) -> Optional[Series]:
         """
 
         if not self._include_index or self._df.index.empty:
-            return None
-        name = self._include_index
-        values, infer_type = self._convert_to_series_values_and_type(self._df.index)
-        return self._convert_to_series(name, values, infer_type)
-
-    def _get_series_from_column(self, column_name: str) -> Series:
-        column = self._df[column_name]
-        values, infer_type = self._convert_to_series_values_and_type(column)
-        return self._convert_to_series(column_name, values, infer_type)
+            return []
+        df = self._pd.DataFrame({self._include_index: self._df.index})
+        index_series_converter = PandasDataFrameConverter(
+            df, self._default_measure_value, self._default_dimension_value
+        )
+        return index_series_converter.get_series_list()
 
     def _get_pandas(self) -> ModuleType:
         try:
@@ -100,31 +103,43 @@ def _get_pandas(self) -> ModuleType:
                 "pandas is not available. Please install pandas to use this feature."
             ) from error
 
-    def _get_df(self, df: Union["pd.DataFrame", "pd.Series"]) -> "pd.DataFrame":  # type: ignore
-        if isinstance(df, self._pd.DataFrame):
-            return df
-        if isinstance(df, self._pd.Series):
-            return self._pd.DataFrame(df)
-        if isinstance(df, type(None)):
+    def _convert_to_df(self, series: "pandas.Series") -> "pandas.Dataframe":  # type: ignore
+        if series.empty:
             return self._pd.DataFrame()
-        raise TypeError("df must be an instance of pandas.DataFrame or pandas.Series")
+        return self._pd.DataFrame(series)
+
+    def _get_sampled_df(self, df: "pandas.DataFrame") -> "pandas.DataFrame":  # type: ignore
+        row_number = len(df)
+        if row_number > self._max_rows:
+            frac = self._max_rows / row_number
+            sampled_df = df.sample(
+                replace=False,
+                frac=frac,
+                random_state=42,
+            )
+            return sampled_df
+        return df
+
+    def _get_columns(self) -> List[str]:
+        return self._df.columns
 
     def _convert_to_series_values_and_type(
-        self, obj: "pd.Series"  # type: ignore
+        self, obj: str  # type: ignore
     ) -> Tuple[SeriesValues, InferType]:
-        column = obj
+        column_name = obj
+        column = self._df[column_name]
         if self._pd.api.types.is_numeric_dtype(column.dtype):
             return self._convert_to_measure_values(column), InferType.MEASURE
         return self._convert_to_dimension_values(column), InferType.DIMENSION
 
     def _convert_to_measure_values(
-        self, obj: "pd.Series"  # type: ignore
+        self, obj: "pandas.DataFrame"  # type: ignore
     ) -> List[MeasureValue]:
         column = obj
         return column.fillna(self._default_measure_value).astype(float).values.tolist()
 
     def _convert_to_dimension_values(
-        self, obj: "pd.Series"  # type: ignore
+        self, obj: "pandas.DataFrame"  # type: ignore
     ) -> List[DimensionValue]:
         column = obj
         return column.fillna(self._default_dimension_value).astype(str).values.tolist()
diff --git a/src/ipyvizzu/data/converters/pandas/protocol.py b/src/ipyvizzu/data/converters/pandas/protocol.py
new file mode 100644
index 000000000..eb2fa79f5
--- /dev/null
+++ b/src/ipyvizzu/data/converters/pandas/protocol.py
@@ -0,0 +1,45 @@
+"""
+This module provides protocol classes for pandas data frame converter.
+"""
+
+from typing import Any, Callable, Sequence
+
+from ipyvizzu.__version__ import PYENV
+
+
+if PYENV >= (3, 8):
+    from typing import Protocol, runtime_checkable
+else:
+    # TODO: remove once support for Python 3.7 is dropped
+    # pylint: disable=duplicate-code
+    from typing_extensions import Protocol, runtime_checkable  # type: ignore
+
+
+@runtime_checkable
+class PandasDataFrame(Protocol):
+    """
+    Represents a pandas DataFrame Protocol.
+    """
+
+    # pylint: disable=too-few-public-methods
+
+    index: Any
+    columns: Sequence[str]
+    sample: Callable[..., Any]
+    __len__: Callable[[], int]
+    __getitem__: Callable[[Any], Any]
+
+
+@runtime_checkable
+class PandasSeries(Protocol):
+    """
+    Represents a pandas Series Protocol.
+    """
+
+    # pylint: disable=too-few-public-methods
+
+    index: Any
+    values: Any
+    dtype: Any
+    __len__: Callable[[], int]
+    __getitem__: Callable[[Any], Any]
diff --git a/src/ipyvizzu/data/converters/spark/__init__.py b/src/ipyvizzu/data/converters/spark/__init__.py
new file mode 100644
index 000000000..68ecb8e47
--- /dev/null
+++ b/src/ipyvizzu/data/converters/spark/__init__.py
@@ -0,0 +1,6 @@
+"""
+This module provides modules for pyspark converter.
+"""
+
+from ipyvizzu.data.converters.spark.converter import SparkDataFrameConverter
+from ipyvizzu.data.converters.spark.protocol import SparkDataFrame
diff --git a/src/ipyvizzu/data/converters/spark/converter.py b/src/ipyvizzu/data/converters/spark/converter.py
new file mode 100644
index 000000000..2580de8b5
--- /dev/null
+++ b/src/ipyvizzu/data/converters/spark/converter.py
@@ -0,0 +1,121 @@
+"""
+This module provides the `SparkDataFrameConverter` class,
+which allows converting a `pyspark` `DataFrame`
+into a list of dictionaries representing series.
+"""
+
+from types import ModuleType
+from typing import List, Tuple
+
+from ipyvizzu.data.converters.defaults import NAN_DIMENSION, NAN_MEASURE
+from ipyvizzu.data.converters.df.defaults import MAX_ROWS
+from ipyvizzu.data.converters.df.converter import DataFrameConverter
+from ipyvizzu.data.infer_type import InferType
+from ipyvizzu.data.type_alias import (
+    DimensionValue,
+    MeasureValue,
+    SeriesValues,
+)
+
+
+class SparkDataFrameConverter(DataFrameConverter):
+    """
+    Converts a `pyspark` `DataFrame` into a list of dictionaries representing series.
+    Each dictionary contains information about the series `name`, `values` and `type`.
+
+    Parameters:
+        df: The `pyspark` `DataFrame` to convert.
+        default_measure_value:
+            Default value to use for missing measure values. Defaults to 0.
+        default_dimension_value:
+            Default value to use for missing dimension values. Defaults to an empty string.
+        max_rows: The maximum number of rows to include in the converted series list.
+            If the `df` contains more rows,
+            a random sample of the given number of rows (approximately) will be taken.
+
+    Example:
+        Get series list from `DataFrame` columns:
+
+            converter = SparkDataFrameConverter(df)
+            series_list = converter.get_series_list()
+    """
+
+    # pylint: disable=too-few-public-methods
+
+    def __init__(
+        self,
+        df: "pyspark.sql.DataFrame",  # type: ignore
+        default_measure_value: MeasureValue = NAN_MEASURE,
+        default_dimension_value: DimensionValue = NAN_DIMENSION,
+        max_rows: int = MAX_ROWS,
+    ) -> None:
+        super().__init__(default_measure_value, default_dimension_value, max_rows)
+        self._pyspark, self._pyspark_func = self._get_pyspark()
+        self._df = self._get_sampled_df(df)
+
+    def _get_pyspark(self) -> Tuple[ModuleType, ModuleType]:
+        try:
+            import pyspark  # pylint: disable=import-outside-toplevel
+            from pyspark.sql import functions  # pylint: disable=import-outside-toplevel
+
+            return pyspark, functions
+        except ImportError as error:
+            raise ImportError(
+                "pyspark is not available. Please install pyspark to use this feature."
+            ) from error
+
+    def _get_sampled_df(
+        self, df: "pyspark.sql.DataFrame"  # type: ignore
+    ) -> "pyspark.sql.DataFrame":  # type: ignore
+        row_number = df.count()
+        if row_number > self._max_rows:
+            fraction = self._max_rows / row_number
+            sample_df = df.sample(withReplacement=False, fraction=fraction, seed=42)
+            return sample_df.limit(self._max_rows)
+        return df
+
+    def _get_columns(self) -> List[str]:
+        return self._df.columns
+
+    def _convert_to_series_values_and_type(
+        self, obj: str
+    ) -> Tuple[SeriesValues, InferType]:
+        column_name = obj
+        column = self._df.select(column_name)
+        integer_type = self._pyspark.sql.types.IntegerType
+        double_type = self._pyspark.sql.types.DoubleType
+        if isinstance(column.schema[column_name].dataType, (integer_type, double_type)):
+            return self._convert_to_measure_values(column_name), InferType.MEASURE
+        return self._convert_to_dimension_values(column_name), InferType.DIMENSION
+
+    def _convert_to_measure_values(self, obj: str) -> List[MeasureValue]:
+        column_name = obj
+        func = self._pyspark_func
+        df = self._df.withColumn(
+            column_name,
+            func.when(
+                func.col(column_name).isNull(), self._default_measure_value
+            ).otherwise(func.col(column_name)),
+        )
+        df_rdd = (
+            df.withColumn(column_name, func.col(column_name).cast("float"))
+            .select(column_name)
+            .rdd
+        )
+        return df_rdd.flatMap(list).collect()
+
+    def _convert_to_dimension_values(self, obj: str) -> List[DimensionValue]:
+        column_name = obj
+        func = self._pyspark_func
+        df = self._df.withColumn(
+            column_name,
+            func.when(
+                func.col(column_name).isNull(), self._default_dimension_value
+            ).otherwise(func.col(column_name)),
+        )
+        df_rdd = (
+            df.withColumn(column_name, func.col(column_name).cast("string"))
+            .select(column_name)
+            .rdd
+        )
+        return df_rdd.flatMap(list).collect()
diff --git a/src/ipyvizzu/data/converters/spark/protocol.py b/src/ipyvizzu/data/converters/spark/protocol.py
new file mode 100644
index 000000000..84e001376
--- /dev/null
+++ b/src/ipyvizzu/data/converters/spark/protocol.py
@@ -0,0 +1,32 @@
+"""
+This module provides protocol classes for pandas data frame converter.
+"""
+
+from typing import Any, Callable, Sequence
+
+from ipyvizzu.__version__ import PYENV
+
+
+if PYENV >= (3, 8):
+    from typing import Protocol, runtime_checkable
+else:
+    # TODO: remove once support for Python 3.7 is dropped
+    # pylint: disable=duplicate-code
+    from typing_extensions import Protocol, runtime_checkable  # type: ignore
+
+
+@runtime_checkable
+class SparkDataFrame(Protocol):
+    """
+    Represents a pyspark DataFrame Protocol.
+    """
+
+    # pylint: disable=too-few-public-methods
+
+    columns: Sequence[str]
+    count: Callable[..., int]
+    sample: Callable[..., Any]
+    limit: Callable[..., Any]
+    select: Callable[..., Any]
+    withColumn: Callable[..., Any]
+    rdd: Any
diff --git a/tests/assets/data_from_json.json b/tests/assets/data_from_json.json
deleted file mode 100644
index 8ef0a6757..000000000
--- a/tests/assets/data_from_json.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-  "dimensions": [
-    { "name": "Genres", "values": ["Rock", "Pop"] },
-    { "name": "Kinds", "values": ["Hard"] }
-  ],
-  "measures": [
-    {
-      "name": "Popularity",
-      "values": [[114, 96]]
-    }
-  ]
-}
diff --git a/tests/assets/df_na.csv b/tests/assets/df_na.csv
deleted file mode 100644
index fe86c35ec..000000000
--- a/tests/assets/df_na.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-Popularity,PopularityAsDimension
-100,
-,100
\ No newline at end of file
diff --git a/tests/assets/in_json.json b/tests/assets/in_json.json
new file mode 100644
index 000000000..7dab9fff1
--- /dev/null
+++ b/tests/assets/in_json.json
@@ -0,0 +1,14 @@
+{
+  "series": [
+    {
+      "name": "DimensionSeries",
+      "type": "dimension",
+      "values": ["1", "2"]
+    },
+    {
+      "name": "MeasureSeries",
+      "type": "measure",
+      "values": [3.0, 4.0]
+    }
+  ]
+}
diff --git a/tests/assets/df_in.json b/tests/assets/in_pd_df_by_series.json
similarity index 83%
rename from tests/assets/df_in.json
rename to tests/assets/in_pd_df_by_series.json
index be76e7948..bed7f2841 100644
--- a/tests/assets/df_in.json
+++ b/tests/assets/in_pd_df_by_series.json
@@ -27,6 +27,5 @@
     "Experimental",
     "Experimental"
   ],
-  "Popularity": [114, 96, 78, 52, 56, 36, 174, 121, 127, 83, 94, 58],
-  "PopularityAsDimension": [114, 96, 78, 52, 56, 36, 174, 121, 127, 83, 94, 58]
+  "Popularity": [114, 96, 78, 52, 56, 36, 174, 121, 127, 83, 94, 58]
 }
diff --git a/tests/assets/in_pd_df_by_series_with_index.json b/tests/assets/in_pd_df_by_series_with_index.json
new file mode 100644
index 000000000..6c7488322
--- /dev/null
+++ b/tests/assets/in_pd_df_by_series_with_index.json
@@ -0,0 +1,45 @@
+{
+  "Index": [
+    "101",
+    "102",
+    "103",
+    "104",
+    "105",
+    "106",
+    "107",
+    "108",
+    "109",
+    "110",
+    "111",
+    "112"
+  ],
+  "Genres": [
+    "Pop",
+    "Rock",
+    "Jazz",
+    "Metal",
+    "Pop",
+    "Rock",
+    "Jazz",
+    "Metal",
+    "Pop",
+    "Rock",
+    "Jazz",
+    "Metal"
+  ],
+  "Kinds": [
+    "Hard",
+    "Hard",
+    "Hard",
+    "Hard",
+    "Smooth",
+    "Experimental",
+    "Smooth",
+    "Smooth",
+    "Experimental",
+    "Experimental",
+    "Experimental",
+    "Experimental"
+  ],
+  "Popularity": [114, 96, 78, 52, 56, 36, 174, 121, 127, 83, 94, 58]
+}
diff --git a/tests/assets/in_pd_df_by_series_with_nan.json b/tests/assets/in_pd_df_by_series_with_nan.json
new file mode 100644
index 000000000..f4a478a75
--- /dev/null
+++ b/tests/assets/in_pd_df_by_series_with_nan.json
@@ -0,0 +1,31 @@
+{
+  "Genres": [
+    "Pop",
+    "Rock",
+    "Jazz",
+    "Metal",
+    "Pop",
+    "Rock",
+    "Jazz",
+    "Metal",
+    "Pop",
+    "Rock",
+    null,
+    "Metal"
+  ],
+  "Kinds": [
+    "Hard",
+    "Hard",
+    "Hard",
+    "Hard",
+    "Smooth",
+    "Experimental",
+    "Smooth",
+    "Smooth",
+    "Experimental",
+    "Experimental",
+    "Experimental",
+    "Experimental"
+  ],
+  "Popularity": [114, 96, 78, 52, 56, 36, 174, 121, 127, 83, null, 58]
+}
diff --git a/tests/assets/fugue_preset.txt b/tests/assets/ref_fugue_preset.txt
similarity index 100%
rename from tests/assets/fugue_preset.txt
rename to tests/assets/ref_fugue_preset.txt
diff --git a/tests/assets/fugue_timeline.txt b/tests/assets/ref_fugue_timeline.txt
similarity index 100%
rename from tests/assets/fugue_timeline.txt
rename to tests/assets/ref_fugue_timeline.txt
diff --git a/tests/assets/ref_pd_df_by_series_max_rows.json b/tests/assets/ref_pd_df_by_series_max_rows.json
new file mode 100644
index 000000000..10dde5366
--- /dev/null
+++ b/tests/assets/ref_pd_df_by_series_max_rows.json
@@ -0,0 +1,21 @@
+{
+  "data": {
+    "series": [
+      {
+        "name": "Genres",
+        "values": ["Jazz", "Rock"],
+        "type": "dimension"
+      },
+      {
+        "name": "Kinds",
+        "values": ["Experimental", "Experimental"],
+        "type": "dimension"
+      },
+      {
+        "name": "Popularity",
+        "values": [94.0, 83.0],
+        "type": "measure"
+      }
+    ]
+  }
+}
diff --git a/tests/assets/df_out.json b/tests/assets/ref_pd_df_by_series_with_duplicated_popularity.json
similarity index 100%
rename from tests/assets/df_out.json
rename to tests/assets/ref_pd_df_by_series_with_duplicated_popularity.json
diff --git a/tests/assets/ref_pd_df_by_series_with_index.json b/tests/assets/ref_pd_df_by_series_with_index.json
new file mode 100644
index 000000000..220d971f9
--- /dev/null
+++ b/tests/assets/ref_pd_df_by_series_with_index.json
@@ -0,0 +1,68 @@
+{
+  "data": {
+    "series": [
+      {
+        "name": "Index",
+        "type": "dimension",
+        "values": [
+          "101",
+          "102",
+          "103",
+          "104",
+          "105",
+          "106",
+          "107",
+          "108",
+          "109",
+          "110",
+          "111",
+          "112"
+        ]
+      },
+      {
+        "name": "Genres",
+        "type": "dimension",
+        "values": [
+          "Pop",
+          "Rock",
+          "Jazz",
+          "Metal",
+          "Pop",
+          "Rock",
+          "Jazz",
+          "Metal",
+          "Pop",
+          "Rock",
+          "Jazz",
+          "Metal"
+        ]
+      },
+      {
+        "name": "Kinds",
+        "type": "dimension",
+        "values": [
+          "Hard",
+          "Hard",
+          "Hard",
+          "Hard",
+          "Smooth",
+          "Experimental",
+          "Smooth",
+          "Smooth",
+          "Experimental",
+          "Experimental",
+          "Experimental",
+          "Experimental"
+        ]
+      },
+      {
+        "name": "Popularity",
+        "type": "measure",
+        "values": [
+          114.0, 96.0, 78.0, 52.0, 56.0, 36.0, 174.0, 121.0, 127.0, 83.0, 94.0,
+          58.0
+        ]
+      }
+    ]
+  }
+}
diff --git a/tests/assets/ref_pd_df_by_series_with_nan.json b/tests/assets/ref_pd_df_by_series_with_nan.json
new file mode 100644
index 000000000..96d88b461
--- /dev/null
+++ b/tests/assets/ref_pd_df_by_series_with_nan.json
@@ -0,0 +1,50 @@
+{
+  "data": {
+    "series": [
+      {
+        "name": "Genres",
+        "type": "dimension",
+        "values": [
+          "Pop",
+          "Rock",
+          "Jazz",
+          "Metal",
+          "Pop",
+          "Rock",
+          "Jazz",
+          "Metal",
+          "Pop",
+          "Rock",
+          "",
+          "Metal"
+        ]
+      },
+      {
+        "name": "Kinds",
+        "type": "dimension",
+        "values": [
+          "Hard",
+          "Hard",
+          "Hard",
+          "Hard",
+          "Smooth",
+          "Experimental",
+          "Smooth",
+          "Smooth",
+          "Experimental",
+          "Experimental",
+          "Experimental",
+          "Experimental"
+        ]
+      },
+      {
+        "name": "Popularity",
+        "type": "measure",
+        "values": [
+          114.0, 96.0, 78.0, 52.0, 56.0, 36.0, 174.0, 121.0, 127.0, 83.0, 0.0,
+          58.0
+        ]
+      }
+    ]
+  }
+}
diff --git a/tests/assets/ref_pd_series.json b/tests/assets/ref_pd_series.json
new file mode 100644
index 000000000..e876e183b
--- /dev/null
+++ b/tests/assets/ref_pd_series.json
@@ -0,0 +1,16 @@
+{
+  "data": {
+    "series": [
+      {
+        "name": "DimensionSeries",
+        "type": "dimension",
+        "values": ["1", "2"]
+      },
+      {
+        "name": "MeasureSeries",
+        "type": "measure",
+        "values": [3.0, 4.0]
+      }
+    ]
+  }
+}
diff --git a/tests/assets/ref_pd_series_only_index.json b/tests/assets/ref_pd_series_only_index.json
new file mode 100644
index 000000000..95ae839f6
--- /dev/null
+++ b/tests/assets/ref_pd_series_only_index.json
@@ -0,0 +1,16 @@
+{
+  "data": {
+    "series": [
+      {
+        "name": "DimensionIndex",
+        "type": "dimension",
+        "values": ["x1", "x2"]
+      },
+      {
+        "name": "MeasureIndex",
+        "type": "dimension",
+        "values": ["y1", "y2"]
+      }
+    ]
+  }
+}
diff --git a/tests/assets/ref_pd_series_with_index.json b/tests/assets/ref_pd_series_with_index.json
new file mode 100644
index 000000000..bb7c2e3fe
--- /dev/null
+++ b/tests/assets/ref_pd_series_with_index.json
@@ -0,0 +1,26 @@
+{
+  "data": {
+    "series": [
+      {
+        "name": "DimensionIndex",
+        "type": "dimension",
+        "values": ["x1", "x2"]
+      },
+      {
+        "name": "DimensionSeries",
+        "type": "dimension",
+        "values": ["1", "2"]
+      },
+      {
+        "name": "MeasureIndex",
+        "type": "dimension",
+        "values": ["y1", "y2"]
+      },
+      {
+        "name": "MeasureSeries",
+        "type": "measure",
+        "values": [3.0, 4.0]
+      }
+    ]
+  }
+}
diff --git a/tests/assets/ref_pd_series_with_nan.json b/tests/assets/ref_pd_series_with_nan.json
new file mode 100644
index 000000000..f03cf9a55
--- /dev/null
+++ b/tests/assets/ref_pd_series_with_nan.json
@@ -0,0 +1,16 @@
+{
+  "data": {
+    "series": [
+      {
+        "name": "DimensionSeries",
+        "type": "dimension",
+        "values": ["1", ""]
+      },
+      {
+        "name": "MeasureSeries",
+        "type": "measure",
+        "values": [3.0, 0.0]
+      }
+    ]
+  }
+}
diff --git a/tests/test_animation.py b/tests/test_animation.py
index caccfc3e2..f6870103d 100644
--- a/tests/test_animation.py
+++ b/tests/test_animation.py
@@ -1,16 +1,8 @@
 # pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
 
 import json
-import pathlib
-from typing import List
 import unittest
 
-import jsonschema  # type: ignore
-import numpy as np
-import pandas as pd
-
-from ipyvizzu.data.type_alias import Record
-
 from ipyvizzu import (
     Animation,
     AnimationMerger,
@@ -22,8 +14,6 @@
     Style,
 )
 
-from tests.utils.import_error import RaiseImportError
-
 
 class TestPlainAnimation(unittest.TestCase):
     def test_plainanimation(self) -> None:
@@ -31,601 +21,6 @@ def test_plainanimation(self) -> None:
         self.assertEqual({"geometry": "circle"}, animation.build())
 
 
-class TestDataSchema(unittest.TestCase):
-    def setUp(self) -> None:
-        self.data = Data()
-
-    def test_schema_dimension_only(self) -> None:
-        self.data.add_dimension("Genres", ["Pop", "Rock"])
-        with self.assertRaises(jsonschema.ValidationError):
-            self.data.build()
-
-    def test_schema_measure_only(self) -> None:
-        self.data.add_measure("Popularity", [[114, 96]])
-        with self.assertRaises(jsonschema.ValidationError):
-            self.data.build()
-
-    def test_schema_data_cube_and_series(self) -> None:
-        self.data.add_dimension("Genres", ["Pop", "Rock"])
-        self.data.add_measure("Popularity", [[114, 96]])
-        self.data.add_series("Kinds", ["Hard"])
-        with self.assertRaises(jsonschema.ValidationError):
-            self.data.build()
-
-    def test_schema_data_cube_and_records(self) -> None:
-        self.data.add_dimension("Genres", ["Pop", "Rock"])
-        self.data.add_measure("Popularity", [[114, 96]])
-        self.data.add_records([["Rock", "Hard", 96], ["Pop", "Hard", 114]])
-        with self.assertRaises(jsonschema.ValidationError):
-            self.data.build()
-
-
-class TestDataClassmethods(unittest.TestCase):
-    asset_dir: pathlib.Path
-
-    @classmethod
-    def setUpClass(cls) -> None:
-        cls.asset_dir = pathlib.Path(__file__).parent / "assets"
-
-    def test_filter(self) -> None:
-        data = Data.filter("filter_expr")
-        # instead of build() test with dump() because contains raw js
-        self.assertEqual(
-            '{"data": {"filter": record => { return (filter_expr) }}}',
-            data.dump(),
-        )
-
-    def test_filter_multiline(self) -> None:
-        filter_expr = """
-        A && 
-            B ||
-            C
-        """
-        data = Data.filter(filter_expr)
-        # instead of build() test with dump() because contains raw js
-        self.assertEqual(
-            '{"data": {"filter": record => { return (A && B || C) }}}',
-            data.dump(),
-        )
-
-    def test_filter_can_be_none(self) -> None:
-        data = Data.filter(None)
-        # instead of build() test with dump() because contains raw js
-        self.assertEqual(
-            '{"data": {"filter": null}}',
-            data.dump(),
-        )
-
-    def test_from_json(self) -> None:
-        data = Data.from_json(self.asset_dir / "data_from_json.json")
-        self.assertEqual(
-            {
-                "data": {
-                    "dimensions": [
-                        {"name": "Genres", "values": ["Rock", "Pop"]},
-                        {"name": "Kinds", "values": ["Hard"]},
-                    ],
-                    "measures": [{"name": "Popularity", "values": [[114, 96]]}],
-                }
-            },
-            data.build(),
-        )
-
-
-class TestData(unittest.TestCase):
-    def setUp(self) -> None:
-        self.data = Data()
-
-    def test_set_filter(self) -> None:
-        self.data.add_records([["Rock", "Hard", 96], ["Pop", "Hard", 114]])
-        self.data.set_filter("filter_expr")
-        self.assertEqual(
-            '{"data": {"records": '
-            + '[["Rock", "Hard", 96], ["Pop", "Hard", 114]], '
-            + '"filter": record => { return (filter_expr) }}}',
-            self.data.dump(),
-        )
-
-    def test_set_filter_can_be_none(self) -> None:
-        self.data.add_records([["Rock", "Hard", 96], ["Pop", "Hard", 114]])
-        self.data.set_filter(None)
-        self.assertEqual(
-            '{"data": {"records": [["Rock", "Hard", 96], ["Pop", "Hard", 114]], "filter": null}}',
-            self.data.dump(),
-        )
-
-    def test_record_list(self) -> None:
-        self.data.add_record(["Rock", "Hard", 96])
-        self.data.add_record(["Pop", "Hard", 114])
-        self.assertEqual(
-            {"data": {"records": [["Rock", "Hard", 96], ["Pop", "Hard", 114]]}},
-            self.data.build(),
-        )
-
-    def test_record_dict(self) -> None:
-        self.data.add_record({"Genres": "Rock", "Kinds": "Hard", "Popularity": 96})
-        self.data.add_record({"Genres": "Pop", "Kinds": "Hard", "Popularity": 114})
-        self.assertEqual(
-            {
-                "data": {
-                    "records": [
-                        {"Genres": "Rock", "Kinds": "Hard", "Popularity": 96},
-                        {"Genres": "Pop", "Kinds": "Hard", "Popularity": 114},
-                    ]
-                }
-            },
-            self.data.build(),
-        )
-
-    def test_records(self) -> None:
-        self.data.add_records([["Rock", "Hard", 96], ["Pop", "Hard", 114]])
-        self.assertEqual(
-            {"data": {"records": [["Rock", "Hard", 96], ["Pop", "Hard", 114]]}},
-            self.data.build(),
-        )
-
-    def test_series(self) -> None:
-        self.data.add_series("Genres", ["Rock", "Pop"], type="dimension")
-        self.data.add_series("Kinds", ["Hard"])
-        self.data.add_series("Popularity", [96, 114], type="measure")
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {
-                            "name": "Genres",
-                            "type": "dimension",
-                            "values": ["Rock", "Pop"],
-                        },
-                        {"name": "Kinds", "values": ["Hard"]},
-                        {"name": "Popularity", "type": "measure", "values": [96, 114]},
-                    ]
-                }
-            },
-            self.data.build(),
-        )
-
-    def test_series_without_values(self) -> None:
-        self.data.add_series("Genres", type="dimension")
-        self.data.add_series("Kinds", type="dimension")
-        self.data.add_series("Popularity", type="measure")
-        records: List[Record] = [["Rock", "Hard", 96], ["Pop", "Hard", 114]]
-        self.data.add_records(records)
-        self.assertEqual(
-            {
-                "data": {
-                    "records": [["Rock", "Hard", 96], ["Pop", "Hard", 114]],
-                    "series": [
-                        {"name": "Genres", "type": "dimension"},
-                        {"name": "Kinds", "type": "dimension"},
-                        {"name": "Popularity", "type": "measure"},
-                    ],
-                }
-            },
-            self.data.build(),
-        )
-
-    def test_data_cube(self) -> None:
-        self.data.add_dimension("Genres", ["Pop", "Rock"])
-        self.data.add_dimension("Kinds", ["Hard"])
-        self.data.add_measure("Popularity", [[114, 96]])
-        self.assertEqual(
-            {
-                "data": {
-                    "dimensions": [
-                        {"name": "Genres", "values": ["Pop", "Rock"]},
-                        {"name": "Kinds", "values": ["Hard"]},
-                    ],
-                    "measures": [
-                        {
-                            "name": "Popularity",
-                            "values": [[114, 96]],
-                        }
-                    ],
-                }
-            },
-            self.data.build(),
-        )
-
-
-class TestDataAddDf(unittest.TestCase):
-    asset_dir: pathlib.Path
-
-    @classmethod
-    def setUpClass(cls) -> None:
-        cls.asset_dir = pathlib.Path(__file__).parent / "assets"
-
-    def setUp(self) -> None:
-        self.data = Data()
-
-    def test_add_df_with_not_df(self) -> None:
-        data = Data()
-        with self.assertRaises(TypeError):
-            data.add_df("")
-
-    def test_add_df_with_none(self) -> None:
-        data = Data()
-        data.add_df(None)
-        self.assertEqual(
-            {"data": {}},
-            data.build(),
-        )
-
-    def test_add_df_with_df(self) -> None:
-        with open(self.asset_dir / "df_in.json", encoding="utf8") as fh_in:
-            fc_in = json.load(fh_in)
-        with open(self.asset_dir / "df_out.json", encoding="utf8") as fh_out:
-            fc_out = json.load(fh_out)
-
-        df = pd.DataFrame(fc_in)
-        df = df.astype({"PopularityAsDimension": str})
-        self.data.add_df(df)
-        self.assertEqual(
-            fc_out,
-            self.data.build(),
-        )
-
-    def test_add_df_with_df_contains_na(self) -> None:
-        df = pd.read_csv(
-            self.asset_dir / "df_na.csv", dtype={"PopularityAsDimension": str}
-        )
-        self.data.add_df(df)
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {
-                            "name": "Popularity",
-                            "type": "measure",
-                            "values": [100.0, 0.0],
-                        },
-                        {
-                            "name": "PopularityAsDimension",
-                            "type": "dimension",
-                            "values": ["", "100"],
-                        },
-                    ]
-                }
-            },
-            self.data.build(),
-        )
-
-    def test_add_df_with_series(self) -> None:
-        data = Data()
-        data.add_df(pd.Series([1, 2], name="series1"))
-        data.add_df(
-            pd.Series({"x": 3, "y": 4, "z": 5}, index=["x", "y"], name="series2")
-        )
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {"name": "series1", "type": "measure", "values": [1.0, 2.0]},
-                        {"name": "series2", "type": "measure", "values": [3.0, 4.0]},
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_df_with_df_and_with_include_index(self) -> None:
-        data = Data()
-        df = pd.DataFrame({"series": [1, 2, 3]}, index=["x", "y", "z"])
-        data.add_df(df, include_index="Index")
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {
-                            "name": "Index",
-                            "type": "dimension",
-                            "values": ["x", "y", "z"],
-                        },
-                        {
-                            "name": "series",
-                            "type": "measure",
-                            "values": [1.0, 2.0, 3.0],
-                        },
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_df_with_series_and_with_include_index(self) -> None:
-        data = Data()
-        df = pd.Series({"x": 1, "y": 2, "z": 3}, index=["x", "y"], name="series")
-        data.add_df(df, include_index="Index")
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {"name": "Index", "type": "dimension", "values": ["x", "y"]},
-                        {"name": "series", "type": "measure", "values": [1.0, 2.0]},
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_df_index(self) -> None:
-        data = Data()
-        df = pd.Series({"x": 1, "y": 2, "z": 3}, index=["x", "y"], name="series")
-        data.add_df_index(df, column_name="Index")
-        data.add_df(df)
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {"name": "Index", "type": "dimension", "values": ["x", "y"]},
-                        {"name": "series", "type": "measure", "values": [1.0, 2.0]},
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_df_index_with_none(self) -> None:
-        data = Data()
-        df = pd.DataFrame()
-        data.add_df_index(df, column_name="Index")
-        data.add_df(df)
-        self.assertEqual(
-            {"data": {}},
-            data.build(),
-        )
-
-    def test_add_df_if_pandas_not_installed(self) -> None:
-        with RaiseImportError.module_name("pandas"):
-            data = Data()
-            with self.assertRaises(ImportError):
-                data.add_df(None)
-
-
-class TestDataAddDataframe(unittest.TestCase):
-    asset_dir: pathlib.Path
-
-    @classmethod
-    def setUpClass(cls) -> None:
-        cls.asset_dir = pathlib.Path(__file__).parent / "assets"
-
-    def setUp(self) -> None:
-        self.data = Data()
-
-    def test_add_data_frame_with_not_df(self) -> None:
-        data = Data()
-        with self.assertRaises(TypeError):
-            data.add_data_frame("")
-
-    def test_add_data_frame_with_none(self) -> None:
-        data = Data()
-        data.add_data_frame(None)
-        self.assertEqual(
-            {"data": {}},
-            data.build(),
-        )
-
-    def test_add_data_frame_with_df(self) -> None:
-        with open(self.asset_dir / "df_in.json", encoding="utf8") as fh_in:
-            fc_in = json.load(fh_in)
-        with open(self.asset_dir / "df_out.json", encoding="utf8") as fh_out:
-            fc_out = json.load(fh_out)
-
-        df = pd.DataFrame(fc_in)
-        df = df.astype({"PopularityAsDimension": str})
-        self.data.add_data_frame(df)
-        self.assertEqual(
-            fc_out,
-            self.data.build(),
-        )
-
-    def test_add_data_frame_with_df_contains_na(self) -> None:
-        df = pd.read_csv(
-            self.asset_dir / "df_na.csv", dtype={"PopularityAsDimension": str}
-        )
-        self.data.add_data_frame(df)
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {
-                            "name": "Popularity",
-                            "type": "measure",
-                            "values": [100.0, 0.0],
-                        },
-                        {
-                            "name": "PopularityAsDimension",
-                            "type": "dimension",
-                            "values": ["", "100"],
-                        },
-                    ]
-                }
-            },
-            self.data.build(),
-        )
-
-    def test_add_data_frame_with_series(self) -> None:
-        data = Data()
-        data.add_data_frame(pd.Series([1, 2], name="series1"))
-        data.add_data_frame(
-            pd.Series({"x": 3, "y": 4, "z": 5}, index=["x", "y"], name="series2")
-        )
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {"name": "series1", "type": "measure", "values": [1.0, 2.0]},
-                        {"name": "series2", "type": "measure", "values": [3.0, 4.0]},
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_data_frame_index(self) -> None:
-        data = Data()
-        df = pd.Series({"x": 1, "y": 2, "z": 3}, index=["x", "y"], name="series")
-        data.add_data_frame_index(df, name="Index")
-        data.add_data_frame(df)
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {"name": "Index", "type": "dimension", "values": ["x", "y"]},
-                        {"name": "series", "type": "measure", "values": [1.0, 2.0]},
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-
-class TestDataAddNpArray(unittest.TestCase):
-    def test_add_np_array_none(self) -> None:
-        data = Data()
-        data.add_np_array(None)
-        self.assertEqual(
-            {"data": {}},
-            data.build(),
-        )
-
-    def test_add_np_array_empty(self) -> None:
-        np_array = np.empty([])
-        data = Data()
-        data.add_np_array(np_array)
-        self.assertEqual(
-            {"data": {}},
-            data.build(),
-        )
-
-    def test_add_np_array1dim(self) -> None:
-        np_array = np.array([127, 128, 129])
-        data = Data()
-        data.add_np_array(np_array)
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {"name": "0", "type": "measure", "values": [127, 128, 129]},
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_np_array1dim_with_str_value(self) -> None:
-        np_array = np.array([127, "128", 129])
-        data = Data()
-        data.add_np_array(np_array)
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {
-                            "name": "0",
-                            "type": "dimension",
-                            "values": ["127", "128", "129"],
-                        },
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_np_array1dim_with_str_and_na_value_and_column_name_and_dtype(
-        self,
-    ) -> None:
-        np_array = np.array([127, "128", np.nan])
-        data = Data()
-        data.add_np_array(np_array, column_name="First", column_dtype=int)
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {
-                            "name": "First",
-                            "type": "measure",
-                            "values": [127, 128, 0],
-                        },
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_np_array2dim(self) -> None:
-        np_array = np.array([[127, 128, 129], [255, 256, 257], [511, 512, 513]])
-        data = Data()
-        data.add_np_array(np_array)
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {"name": "0", "type": "measure", "values": [127, 255, 511]},
-                        {"name": "1", "type": "measure", "values": [128, 256, 512]},
-                        {"name": "2", "type": "measure", "values": [129, 257, 513]},
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_np_array2dim_with_str_and_na_value_and_column_name_and_dtype(
-        self,
-    ) -> None:
-        np_array = np.array([[127, "128", 129], [255, np.nan, 257], [511, 512, 513]])
-        data = Data()
-        data.add_np_array(np_array, column_name={0: "First"}, column_dtype={2: int})
-        self.assertEqual(
-            {
-                "data": {
-                    "series": [
-                        {
-                            "name": "First",
-                            "type": "dimension",
-                            "values": ["127", "255", "511"],
-                        },
-                        {
-                            "name": "1",
-                            "type": "dimension",
-                            "values": ["128", "", "512"],
-                        },
-                        {
-                            "name": "2",
-                            "type": "measure",
-                            "values": [129, 257, 513],
-                        },
-                    ]
-                }
-            },
-            data.build(),
-        )
-
-    def test_add_np_array2dim_with_non_dict_column_name(self) -> None:
-        np_array = np.zeros((2, 2))
-        data = Data()
-        with self.assertRaises(ValueError):
-            data.add_np_array(np_array, column_name="First")
-
-    def test_add_np_array2dim_with_non_dict_column_dtype(self) -> None:
-        np_array = np.zeros((2, 2))
-        data = Data()
-        with self.assertRaises(ValueError):
-            data.add_np_array(np_array, column_dtype=str)
-
-    def test_add_np_array3dim(self) -> None:
-        np_array = np.zeros((3, 3, 3))
-        data = Data()
-        with self.assertRaises(ValueError):
-            data.add_np_array(np_array)
-
-    def test_add_df_if_numpy_not_installed(self) -> None:
-        with RaiseImportError.module_name("numpy"):
-            data = Data()
-            with self.assertRaises(ImportError):
-                data.add_np_array(None)
-
-
 class TestConfig(unittest.TestCase):
     def test_config(self) -> None:
         animation = Config({"color": {"set": ["Genres"]}})
diff --git a/tests/test_data/__init__.py b/tests/test_data/__init__.py
new file mode 100644
index 000000000..61235ddf0
--- /dev/null
+++ b/tests/test_data/__init__.py
@@ -0,0 +1,150 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+import copy
+import json
+from pathlib import Path
+import unittest
+
+import pandas as pd
+
+from ipyvizzu import Data
+
+
+class DataWithAssets(unittest.TestCase):
+    asset_dir: Path
+
+    in_pd_df_by_series: pd.DataFrame
+    ref_pd_df_by_series: dict
+    in_pd_df_by_series_with_duplicated_popularity: pd.DataFrame
+    ref_pd_df_by_series_with_duplicated_popularity: dict
+    in_pd_df_by_series_with_nan: pd.DataFrame
+    ref_pd_df_by_series_with_nan: dict
+    in_pd_df_by_series_with_index: pd.DataFrame
+    ref_pd_df_by_series_with_index: dict
+    ref_pd_df_by_series_only_index: dict
+    ref_pd_df_by_series_max_rows: dict
+
+    in_pd_series_dimension: pd.Series
+    in_pd_series_measure: pd.Series
+    ref_pd_series: dict
+    in_pd_series_dimension_with_nan: pd.Series
+    in_pd_series_measure_with_nan: pd.Series
+    ref_pd_series_with_nan: dict
+    in_pd_series_dimension_with_index: pd.Series
+    in_pd_series_measure_with_index: pd.Series
+    ref_pd_series_with_index: dict
+    ref_pd_series_only_index: dict
+
+    def setUp(self) -> None:
+        self.data = Data()
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.asset_dir = Path(__file__).parent.parent / "assets"
+        cls.set_up_pd_df()
+        cls.set_up_pd_series()
+
+    @classmethod
+    def set_up_pd_df(cls) -> None:
+        with open(cls.asset_dir / "in_pd_df_by_series.json", encoding="utf8") as fh_in:
+            in_pd_df_by_series = json.load(fh_in)
+            cls.in_pd_df_by_series = pd.DataFrame(in_pd_df_by_series)
+
+            in_pd_df_by_series_with_duplicated_popularity = in_pd_df_by_series
+            in_pd_df_by_series_with_duplicated_popularity[
+                "PopularityAsDimension"
+            ] = in_pd_df_by_series_with_duplicated_popularity["Popularity"]
+            in_pd_df_by_series_with_duplicated_popularity = pd.DataFrame(
+                in_pd_df_by_series_with_duplicated_popularity
+            )
+            cls.in_pd_df_by_series_with_duplicated_popularity = (
+                in_pd_df_by_series_with_duplicated_popularity.astype(
+                    {"PopularityAsDimension": str}
+                )
+            )
+
+        with open(
+            cls.asset_dir / "ref_pd_df_by_series_with_duplicated_popularity.json",
+            encoding="utf8",
+        ) as fh_in:
+            cls.ref_pd_df_by_series_with_duplicated_popularity = json.load(fh_in)
+
+            cls.ref_pd_df_by_series = copy.deepcopy(
+                cls.ref_pd_df_by_series_with_duplicated_popularity
+            )
+            cls.ref_pd_df_by_series["data"]["series"] = cls.ref_pd_df_by_series["data"][
+                "series"
+            ][:-1]
+
+        with open(
+            cls.asset_dir / "ref_pd_df_by_series_max_rows.json", encoding="utf8"
+        ) as fh_in:
+            cls.ref_pd_df_by_series_max_rows = json.load(fh_in)
+
+        with open(
+            cls.asset_dir / "in_pd_df_by_series_with_nan.json", encoding="utf8"
+        ) as fh_in:
+            cls.in_pd_df_by_series_with_nan = pd.DataFrame(json.load(fh_in))
+
+        with open(
+            cls.asset_dir / "ref_pd_df_by_series_with_nan.json", encoding="utf8"
+        ) as fh_in:
+            cls.ref_pd_df_by_series_with_nan = json.load(fh_in)
+
+        with open(
+            cls.asset_dir / "in_pd_df_by_series_with_index.json", encoding="utf8"
+        ) as fh_in:
+            in_pd_df_by_series_with_index = pd.DataFrame(json.load(fh_in))
+            cls.in_pd_df_by_series_with_index = in_pd_df_by_series_with_index.set_index(
+                "Index"
+            )
+
+        with open(
+            cls.asset_dir / "ref_pd_df_by_series_with_index.json", encoding="utf8"
+        ) as fh_in:
+            ref_pd_df_by_series_with_index = json.load(fh_in)
+
+            cls.ref_pd_df_by_series_with_index = ref_pd_df_by_series_with_index
+            cls.ref_pd_df_by_series_only_index = copy.deepcopy(
+                ref_pd_df_by_series_with_index
+            )
+            cls.ref_pd_df_by_series_only_index["data"][
+                "series"
+            ] = cls.ref_pd_df_by_series_only_index["data"]["series"][:1]
+
+    @classmethod
+    def set_up_pd_series(cls) -> None:
+        cls.in_pd_series_dimension = pd.Series(["1", "2"], name="DimensionSeries")
+        cls.in_pd_series_measure = pd.Series([3, 4], name="MeasureSeries")
+
+        with open(cls.asset_dir / "ref_pd_series.json", encoding="utf8") as fh_in:
+            cls.ref_pd_series = json.load(fh_in)
+
+        cls.in_pd_series_dimension_with_nan = pd.Series(
+            ["1", None], name="DimensionSeries"
+        )
+        cls.in_pd_series_measure_with_nan = pd.Series([3, None], name="MeasureSeries")
+
+        with open(
+            cls.asset_dir / "ref_pd_series_with_nan.json", encoding="utf8"
+        ) as fh_in:
+            cls.ref_pd_series_with_nan = json.load(fh_in)
+
+        cls.in_pd_series_dimension_with_index = pd.Series(
+            {"x1": "1", "x2": "2", "x3": "3"},
+            index=["x1", "x2"],
+            name="DimensionSeries",
+        )
+        cls.in_pd_series_measure_with_index = pd.Series(
+            {"y1": 3, "y2": 4, "y3": 5}, index=["y1", "y2"], name="MeasureSeries"
+        )
+
+        with open(
+            cls.asset_dir / "ref_pd_series_with_index.json", encoding="utf8"
+        ) as fh_in:
+            cls.ref_pd_series_with_index = json.load(fh_in)
+
+        with open(
+            cls.asset_dir / "ref_pd_series_only_index.json", encoding="utf8"
+        ) as fh_in:
+            cls.ref_pd_series_only_index = json.load(fh_in)
diff --git a/tests/test_data/test_data_datacube.py b/tests/test_data/test_data_datacube.py
new file mode 100644
index 000000000..0a0aab2a8
--- /dev/null
+++ b/tests/test_data/test_data_datacube.py
@@ -0,0 +1,32 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+import unittest
+
+from ipyvizzu import Data
+
+
+class TestDataDataCube(unittest.TestCase):
+    def setUp(self) -> None:
+        self.data = Data()
+
+    def test_data_cube(self) -> None:
+        self.data.add_dimension("Genres", ["Pop", "Rock"])
+        self.data.add_dimension("Kinds", ["Hard"])
+        self.data.add_measure("Popularity", [[114, 96]])
+        self.assertEqual(
+            {
+                "data": {
+                    "dimensions": [
+                        {"name": "Genres", "values": ["Pop", "Rock"]},
+                        {"name": "Kinds", "values": ["Hard"]},
+                    ],
+                    "measures": [
+                        {
+                            "name": "Popularity",
+                            "values": [[114, 96]],
+                        }
+                    ],
+                }
+            },
+            self.data.build(),
+        )
diff --git a/tests/test_data/test_data_filter.py b/tests/test_data/test_data_filter.py
new file mode 100644
index 000000000..21496c9dc
--- /dev/null
+++ b/tests/test_data/test_data_filter.py
@@ -0,0 +1,66 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+import pathlib
+import unittest
+
+from ipyvizzu import Data
+
+
+class TestDataFilter(unittest.TestCase):
+    def setUp(self) -> None:
+        self.data = Data()
+
+    def test_set_filter(self) -> None:
+        self.data.add_records([["Rock", "Hard", 96], ["Pop", "Hard", 114]])
+        self.data.set_filter("filter_expr")
+        self.assertEqual(
+            '{"data": {"records": '
+            + '[["Rock", "Hard", 96], ["Pop", "Hard", 114]], '
+            + '"filter": record => { return (filter_expr) }}}',
+            self.data.dump(),
+        )
+
+    def test_set_filter_can_be_none(self) -> None:
+        self.data.add_records([["Rock", "Hard", 96], ["Pop", "Hard", 114]])
+        self.data.set_filter(None)
+        self.assertEqual(
+            '{"data": {"records": [["Rock", "Hard", 96], ["Pop", "Hard", 114]], "filter": null}}',
+            self.data.dump(),
+        )
+
+
+class TestDataFilterCls(unittest.TestCase):
+    asset_dir: pathlib.Path
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.asset_dir = pathlib.Path(__file__).parent / "assets"
+
+    def test_filter(self) -> None:
+        data = Data.filter("filter_expr")
+        # instead of build() test with dump() because contains raw js
+        self.assertEqual(
+            '{"data": {"filter": record => { return (filter_expr) }}}',
+            data.dump(),
+        )
+
+    def test_filter_multiline(self) -> None:
+        filter_expr = """
+        A && 
+            B ||
+            C
+        """
+        data = Data.filter(filter_expr)
+        # instead of build() test with dump() because contains raw js
+        self.assertEqual(
+            '{"data": {"filter": record => { return (A && B || C) }}}',
+            data.dump(),
+        )
+
+    def test_filter_can_be_none(self) -> None:
+        data = Data.filter(None)
+        # instead of build() test with dump() because contains raw js
+        self.assertEqual(
+            '{"data": {"filter": null}}',
+            data.dump(),
+        )
diff --git a/tests/test_data/test_data_json.py b/tests/test_data/test_data_json.py
new file mode 100644
index 000000000..262600b90
--- /dev/null
+++ b/tests/test_data/test_data_json.py
@@ -0,0 +1,14 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+from ipyvizzu import Data
+
+from tests.test_data import DataWithAssets
+
+
+class TestDataJson(DataWithAssets):
+    def test_from_json(self) -> None:
+        data = Data.from_json(self.asset_dir / "in_json.json")
+        self.assertEqual(
+            self.ref_pd_series,
+            data.build(),
+        )
diff --git a/tests/test_data/test_data_records.py b/tests/test_data/test_data_records.py
new file mode 100644
index 000000000..97db68420
--- /dev/null
+++ b/tests/test_data/test_data_records.py
@@ -0,0 +1,40 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+import unittest
+
+from ipyvizzu import Data
+
+
+class TestDataRecords(unittest.TestCase):
+    def setUp(self) -> None:
+        self.data = Data()
+
+    def test_record_list(self) -> None:
+        self.data.add_record(["Rock", "Hard", 96])
+        self.data.add_record(["Pop", "Hard", 114])
+        self.assertEqual(
+            {"data": {"records": [["Rock", "Hard", 96], ["Pop", "Hard", 114]]}},
+            self.data.build(),
+        )
+
+    def test_record_dict(self) -> None:
+        self.data.add_record({"Genres": "Rock", "Kinds": "Hard", "Popularity": 96})
+        self.data.add_record({"Genres": "Pop", "Kinds": "Hard", "Popularity": 114})
+        self.assertEqual(
+            {
+                "data": {
+                    "records": [
+                        {"Genres": "Rock", "Kinds": "Hard", "Popularity": 96},
+                        {"Genres": "Pop", "Kinds": "Hard", "Popularity": 114},
+                    ]
+                }
+            },
+            self.data.build(),
+        )
+
+    def test_records(self) -> None:
+        self.data.add_records([["Rock", "Hard", 96], ["Pop", "Hard", 114]])
+        self.assertEqual(
+            {"data": {"records": [["Rock", "Hard", 96], ["Pop", "Hard", 114]]}},
+            self.data.build(),
+        )
diff --git a/tests/test_data/test_data_schema.py b/tests/test_data/test_data_schema.py
new file mode 100644
index 000000000..0b76e6fd2
--- /dev/null
+++ b/tests/test_data/test_data_schema.py
@@ -0,0 +1,36 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+import unittest
+
+import jsonschema  # type: ignore
+
+from ipyvizzu import Data
+
+
+class TestDataSchema(unittest.TestCase):
+    def setUp(self) -> None:
+        self.data = Data()
+
+    def test_schema_dimension_only(self) -> None:
+        self.data.add_dimension("Genres", ["Pop", "Rock"])
+        with self.assertRaises(jsonschema.ValidationError):
+            self.data.build()
+
+    def test_schema_measure_only(self) -> None:
+        self.data.add_measure("Popularity", [[114, 96]])
+        with self.assertRaises(jsonschema.ValidationError):
+            self.data.build()
+
+    def test_schema_data_cube_and_series(self) -> None:
+        self.data.add_dimension("Genres", ["Pop", "Rock"])
+        self.data.add_measure("Popularity", [[114, 96]])
+        self.data.add_series("Kinds", ["Hard"])
+        with self.assertRaises(jsonschema.ValidationError):
+            self.data.build()
+
+    def test_schema_data_cube_and_records(self) -> None:
+        self.data.add_dimension("Genres", ["Pop", "Rock"])
+        self.data.add_measure("Popularity", [[114, 96]])
+        self.data.add_records([["Rock", "Hard", 96], ["Pop", "Hard", 114]])
+        with self.assertRaises(jsonschema.ValidationError):
+            self.data.build()
diff --git a/tests/test_data/test_data_series.py b/tests/test_data/test_data_series.py
new file mode 100644
index 000000000..45f1619bc
--- /dev/null
+++ b/tests/test_data/test_data_series.py
@@ -0,0 +1,54 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+from typing import List
+import unittest
+
+from ipyvizzu.data.type_alias import Record
+
+from ipyvizzu import Data
+
+
+class TestDataSeries(unittest.TestCase):
+    def setUp(self) -> None:
+        self.data = Data()
+
+    def test_series(self) -> None:
+        self.data.add_series("Genres", ["Rock", "Pop"], type="dimension")
+        self.data.add_series("Kinds", ["Hard"])
+        self.data.add_series("Popularity", [96, 114], type="measure")
+        self.assertEqual(
+            {
+                "data": {
+                    "series": [
+                        {
+                            "name": "Genres",
+                            "type": "dimension",
+                            "values": ["Rock", "Pop"],
+                        },
+                        {"name": "Kinds", "values": ["Hard"]},
+                        {"name": "Popularity", "type": "measure", "values": [96, 114]},
+                    ]
+                }
+            },
+            self.data.build(),
+        )
+
+    def test_series_without_values(self) -> None:
+        self.data.add_series("Genres", type="dimension")
+        self.data.add_series("Kinds", type="dimension")
+        self.data.add_series("Popularity", type="measure")
+        records: List[Record] = [["Rock", "Hard", 96], ["Pop", "Hard", 114]]
+        self.data.add_records(records)
+        self.assertEqual(
+            {
+                "data": {
+                    "records": [["Rock", "Hard", 96], ["Pop", "Hard", 114]],
+                    "series": [
+                        {"name": "Genres", "type": "dimension"},
+                        {"name": "Kinds", "type": "dimension"},
+                        {"name": "Popularity", "type": "measure"},
+                    ],
+                }
+            },
+            self.data.build(),
+        )
diff --git a/tests/test_data/test_numpy.py b/tests/test_data/test_numpy.py
new file mode 100644
index 000000000..f9010470f
--- /dev/null
+++ b/tests/test_data/test_numpy.py
@@ -0,0 +1,149 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+import unittest
+
+import numpy as np
+
+from ipyvizzu import Data
+
+from tests.utils.import_error import RaiseImportError
+
+
+class TestDataNpArray(unittest.TestCase):
+    def setUp(self) -> None:
+        self.data = Data()
+
+    def test_add_np_array_if_numpy_not_installed(self) -> None:
+        with RaiseImportError.module_name("numpy"):
+            with self.assertRaises(ImportError):
+                self.data.add_np_array(np.empty(()))
+
+    def test_add_np_array_none(self) -> None:
+        self.data.add_np_array(None)
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_np_array_empty(self) -> None:
+        np_array = np.empty([])
+        self.data.add_np_array(np_array)
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_np_array1dim(self) -> None:
+        np_array = np.array([127, 128, 129])
+        self.data.add_np_array(np_array)
+        self.assertEqual(
+            {
+                "data": {
+                    "series": [
+                        {"name": "0", "type": "measure", "values": [127, 128, 129]},
+                    ]
+                }
+            },
+            self.data.build(),
+        )
+
+    def test_add_np_array1dim_with_str_value(self) -> None:
+        np_array = np.array([127, "128", 129])
+        self.data.add_np_array(np_array)
+        self.assertEqual(
+            {
+                "data": {
+                    "series": [
+                        {
+                            "name": "0",
+                            "type": "dimension",
+                            "values": ["127", "128", "129"],
+                        },
+                    ]
+                }
+            },
+            self.data.build(),
+        )
+
+    def test_add_np_array1dim_with_str_and_na_value_and_column_name_and_dtype(
+        self,
+    ) -> None:
+        np_array = np.array([127, "128", np.nan])
+        self.data.add_np_array(np_array, column_name="First", column_dtype=int)
+        self.assertEqual(
+            {
+                "data": {
+                    "series": [
+                        {
+                            "name": "First",
+                            "type": "measure",
+                            "values": [127, 128, 0],
+                        },
+                    ]
+                }
+            },
+            self.data.build(),
+        )
+
+    def test_add_np_array2dim(self) -> None:
+        np_array = np.array([[127, 128, 129], [255, 256, 257], [511, 512, 513]])
+        self.data.add_np_array(np_array)
+        self.assertEqual(
+            {
+                "data": {
+                    "series": [
+                        {"name": "0", "type": "measure", "values": [127, 255, 511]},
+                        {"name": "1", "type": "measure", "values": [128, 256, 512]},
+                        {"name": "2", "type": "measure", "values": [129, 257, 513]},
+                    ]
+                }
+            },
+            self.data.build(),
+        )
+
+    def test_add_np_array2dim_with_str_and_na_value_and_column_name_and_dtype(
+        self,
+    ) -> None:
+        np_array = np.array([[127, "128", 129], [255, np.nan, 257], [511, 512, 513]])
+        self.data.add_np_array(
+            np_array, column_name={0: "First"}, column_dtype={2: int}
+        )
+        self.assertEqual(
+            {
+                "data": {
+                    "series": [
+                        {
+                            "name": "First",
+                            "type": "dimension",
+                            "values": ["127", "255", "511"],
+                        },
+                        {
+                            "name": "1",
+                            "type": "dimension",
+                            "values": ["128", "", "512"],
+                        },
+                        {
+                            "name": "2",
+                            "type": "measure",
+                            "values": [129, 257, 513],
+                        },
+                    ]
+                }
+            },
+            self.data.build(),
+        )
+
+    def test_add_np_array2dim_with_non_dict_column_name(self) -> None:
+        np_array = np.zeros((2, 2))
+        with self.assertRaises(ValueError):
+            self.data.add_np_array(np_array, column_name="First")
+
+    def test_add_np_array2dim_with_non_dict_column_dtype(self) -> None:
+        np_array = np.zeros((2, 2))
+        with self.assertRaises(ValueError):
+            self.data.add_np_array(np_array, column_dtype=str)
+
+    def test_add_np_array3dim(self) -> None:
+        np_array = np.zeros((3, 3, 3))
+        with self.assertRaises(ValueError):
+            self.data.add_np_array(np_array)
diff --git a/tests/test_data/test_pandas.py b/tests/test_data/test_pandas.py
new file mode 100644
index 000000000..96f0deccc
--- /dev/null
+++ b/tests/test_data/test_pandas.py
@@ -0,0 +1,221 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+import pandas as pd
+
+from tests.test_data import DataWithAssets
+from tests.utils.import_error import RaiseImportError
+
+
+class TestDf(DataWithAssets):
+    def test_add_df_if_pandas_not_installed(self) -> None:
+        with RaiseImportError.module_name("pandas"):
+            with self.assertRaises(ImportError):
+                self.data.add_df(pd.DataFrame())
+
+    def test_add_df_with_none(self) -> None:
+        self.data.add_df(None)
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_df_with_empty_df(self) -> None:
+        self.data.add_df(pd.DataFrame())
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_df_with_df(self) -> None:
+        df = self.in_pd_df_by_series_with_duplicated_popularity
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series_with_duplicated_popularity,
+            self.data.build(),
+        )
+
+    def test_add_df_with_df_contains_na(self) -> None:
+        df = self.in_pd_df_by_series_with_nan
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series_with_nan,
+            self.data.build(),
+        )
+
+    def test_add_df_with_df_and_with_include_index(self) -> None:
+        df = self.in_pd_df_by_series_with_index
+        self.data.add_df(df, include_index="Index")
+        self.assertEqual(
+            self.ref_pd_df_by_series_with_index,
+            self.data.build(),
+        )
+
+    def test_add_df_with_df_and_max_rows(self) -> None:
+        df = self.in_pd_df_by_series
+        self.data.add_df(df, max_rows=2)
+        self.assertEqual(
+            self.ref_pd_df_by_series_max_rows,
+            self.data.build(),
+        )
+
+
+class TestDataFrame(DataWithAssets):
+    def test_add_data_frame_with_none(self) -> None:
+        self.data.add_data_frame(None)
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_data_frame_with_empty_df(self) -> None:
+        self.data.add_data_frame(pd.DataFrame())
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_data_frame_with_df(self) -> None:
+        df = self.in_pd_df_by_series_with_duplicated_popularity
+        self.data.add_data_frame(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series_with_duplicated_popularity,
+            self.data.build(),
+        )
+
+    def test_add_data_frame_with_df_contains_na(self) -> None:
+        df = self.in_pd_df_by_series_with_nan
+        self.data.add_data_frame(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series_with_nan,
+            self.data.build(),
+        )
+
+
+class TestDfWithSeries(DataWithAssets):
+    def test_add_df_with_empty_series(self) -> None:
+        self.data.add_df(pd.Series())
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_df_with_series(self) -> None:
+        self.data.add_df(self.in_pd_series_dimension)
+        self.data.add_df(self.in_pd_series_measure)
+        self.assertEqual(
+            self.ref_pd_series,
+            self.data.build(),
+        )
+
+    def test_add_df_with_series_contains_na(self) -> None:
+        self.data.add_df(self.in_pd_series_dimension_with_nan)
+        self.data.add_df(self.in_pd_series_measure_with_nan)
+        self.assertEqual(
+            self.ref_pd_series_with_nan,
+            self.data.build(),
+        )
+
+    def test_add_df_with_series_and_with_include_index(self) -> None:
+        self.data.add_df(
+            self.in_pd_series_dimension_with_index,
+            include_index="DimensionIndex",
+        )
+        self.data.add_df(
+            self.in_pd_series_measure_with_index,
+            include_index="MeasureIndex",
+        )
+        self.assertEqual(
+            self.ref_pd_series_with_index,
+            self.data.build(),
+        )
+
+
+class TestDataFrameWithSeries(DataWithAssets):
+    def test_add_data_frame_with_empty_series(self) -> None:
+        self.data.add_data_frame(pd.Series())
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_data_frame_with_series(self) -> None:
+        self.data.add_data_frame(self.in_pd_series_dimension)
+        self.data.add_data_frame(self.in_pd_series_measure)
+        self.assertEqual(
+            self.ref_pd_series,
+            self.data.build(),
+        )
+
+    def test_add_data_frame_with_series_contains_na(self) -> None:
+        self.data.add_data_frame(self.in_pd_series_dimension_with_nan)
+        self.data.add_data_frame(self.in_pd_series_measure_with_nan)
+        self.assertEqual(
+            self.ref_pd_series_with_nan,
+            self.data.build(),
+        )
+
+
+class TestDfIndex(DataWithAssets):
+    def test_add_df_index_with_none(self) -> None:
+        self.data.add_df_index(None, column_name="Index")
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_df_index_with_df(self) -> None:
+        df = self.in_pd_df_by_series_with_index
+        self.data.add_df_index(df, column_name="Index")
+        self.assertEqual(
+            self.ref_pd_df_by_series_only_index,
+            self.data.build(),
+        )
+
+
+class TestDataFrameIndex(DataWithAssets):
+    def test_add_data_frame_index_with_none(self) -> None:
+        self.data.add_data_frame_index(None, name="Index")
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_data_frame_index_with_df(self) -> None:
+        df = self.in_pd_df_by_series_with_index
+        self.data.add_data_frame_index(df, name="Index")
+        self.assertEqual(
+            self.ref_pd_df_by_series_only_index,
+            self.data.build(),
+        )
+
+
+class TestDfIndexWithSeries(DataWithAssets):
+    def test_add_df_index_with_series(self) -> None:
+        self.data.add_df_index(
+            self.in_pd_series_dimension_with_index,
+            column_name="DimensionIndex",
+        )
+        self.data.add_df_index(
+            self.in_pd_series_measure_with_index,
+            column_name="MeasureIndex",
+        )
+        self.assertEqual(
+            self.ref_pd_series_only_index,
+            self.data.build(),
+        )
+
+
+class TestDataFrameIndexWithSeries(DataWithAssets):
+    def test_add_data_frame_index_with_series(self) -> None:
+        self.data.add_data_frame_index(
+            self.in_pd_series_dimension_with_index,
+            name="DimensionIndex",
+        )
+        self.data.add_data_frame_index(
+            self.in_pd_series_measure_with_index,
+            name="MeasureIndex",
+        )
+        self.assertEqual(
+            self.ref_pd_series_only_index,
+            self.data.build(),
+        )
diff --git a/tests/test_data/test_pyspark.py b/tests/test_data/test_pyspark.py
new file mode 100644
index 000000000..358d3a876
--- /dev/null
+++ b/tests/test_data/test_pyspark.py
@@ -0,0 +1,130 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+from pyspark.sql import SparkSession
+from pyspark.sql.types import StructType, StructField, StringType, IntegerType
+
+from tests.test_data import DataWithAssets
+from tests.utils.import_error import RaiseImportError
+
+
+class TestDataSpark(DataWithAssets):
+    spark: SparkSession
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        super().setUpClass()
+        cls.spark = SparkSession.builder.appName("SparkTest").getOrCreate()
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        super().tearDownClass()
+        cls.spark.stop()
+
+    def test_add_df_if_pyspark_not_installed(self) -> None:
+        with RaiseImportError.module_name("pyspark"):
+            with self.assertRaises(ImportError):
+                self.data.add_df(self.spark.createDataFrame([], StructType([])))
+
+    def test_add_df_with_none(self) -> None:
+        self.data.add_df(None)
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_df_with_empty_df(self) -> None:
+        self.data.add_df(self.spark.createDataFrame([], StructType([])))
+        self.assertEqual(
+            {"data": {}},
+            self.data.build(),
+        )
+
+    def test_add_df_with_df(self) -> None:
+        schema = StructType(
+            [
+                StructField("DimensionSeries", StringType(), True),
+                StructField("MeasureSeries", IntegerType(), True),
+            ]
+        )
+        df_data = [
+            ("1", 3),
+            ("2", 4),
+        ]
+        df = self.spark.createDataFrame(df_data, schema)
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_series,
+            self.data.build(),
+        )
+
+    def test_add_df_with_df_contains_na(self) -> None:
+        schema = StructType(
+            [
+                StructField("DimensionSeries", StringType(), True),
+                StructField("MeasureSeries", IntegerType(), True),
+            ]
+        )
+        df_data = [
+            ("1", 3),
+            (None, None),
+        ]
+        df = self.spark.createDataFrame(df_data, schema)
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_series_with_nan,
+            self.data.build(),
+        )
+
+    def test_add_df_with_df_and_with_include_index(self) -> None:
+        df = self.spark.createDataFrame([], StructType([]))
+        with self.assertRaises(ValueError):
+            self.data.add_df(df, include_index="Index")
+
+    def test_add_df_with_df_and_max_rows(self) -> None:
+        max_rows = 2
+
+        dimension_data = ["0", "1", "2", "3", "4"]
+        measure_data = [0, 1, 2, 3, 4]
+        df_data = []
+        for i, dimension_value in enumerate(dimension_data):
+            measure_value = measure_data[i]
+            df_data.append((dimension_value, measure_value))
+        schema = StructType(
+            [
+                StructField("DimensionSeries", StringType(), True),
+                StructField("MeasureSeries", IntegerType(), True),
+            ]
+        )
+        df = self.spark.createDataFrame(df_data, schema)
+        self.data.add_df(df, max_rows=max_rows)
+
+        data_series = self.data.build()["data"]["series"]
+
+        dimension_series = data_series[0]["values"]
+        measure_series = data_series[1]["values"]
+
+        self.assertTrue(1 <= len(dimension_series) <= max_rows)
+        self.assertTrue(1 <= len(measure_series) <= max_rows)
+
+        is_dimension_series_sublist = all(
+            item in dimension_data for item in dimension_series
+        )
+        is_measure_series_sublist = all(item in measure_data for item in measure_series)
+        self.assertTrue(is_dimension_series_sublist)
+        self.assertTrue(is_measure_series_sublist)
+
+        del data_series[0]["values"]
+        del data_series[1]["values"]
+        self.assertEqual(
+            [
+                {
+                    "name": "DimensionSeries",
+                    "type": "dimension",
+                },
+                {
+                    "name": "MeasureSeries",
+                    "type": "measure",
+                },
+            ],
+            data_series,
+        )
diff --git a/tests/test_doc.py b/tests/test_doc.py
deleted file mode 100644
index b321de07d..000000000
--- a/tests/test_doc.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
-
-import json
-import pathlib
-import sys
-import unittest
-
-import numpy as np
-import pandas as pd
-
-from ipyvizzu import Data
-
-
-class TestData(unittest.TestCase):
-    asset_dir: pathlib.Path
-    doc_dir: pathlib.Path
-
-    @classmethod
-    def setUpClass(cls) -> None:
-        cls.asset_dir = pathlib.Path(__file__).parent / "assets"
-        cls.doc_dir = pathlib.Path(__file__).parent.parent / "docs" / "assets" / "data"
-
-    def setUp(self) -> None:
-        self.data = Data()
-
-    def test_add_df_with_csv(self) -> None:
-        with open(self.asset_dir / "df_out.json", encoding="utf8") as fh_out:
-            fc_out = json.load(fh_out)
-            fc_out["data"]["series"] = fc_out["data"]["series"][:-1]
-
-        df = pd.read_csv(self.doc_dir / "music_data.csv")
-
-        data = Data()
-        data.add_df(df)
-        self.assertEqual(
-            fc_out,
-            data.build(),
-        )
-
-    # TODO: remove decorator once support for Python 3.6 is dropped
-    @unittest.skipUnless(sys.version_info >= (3, 7), "at least Python 3.7 is required")
-    def test_add_df_with_xlsx(self) -> None:
-        with open(self.asset_dir / "df_out.json", encoding="utf8") as fh_out:
-            fc_out = json.load(fh_out)
-            fc_out["data"]["series"] = fc_out["data"]["series"][:-1]
-
-        df = pd.read_excel(self.doc_dir / "music_data.xlsx")
-
-        data = Data()
-        data.add_df(df)
-        self.assertEqual(
-            fc_out,
-            data.build(),
-        )
-
-    def test_add_df_with_googlesheet(self) -> None:
-        with open(self.asset_dir / "df_out.json", encoding="utf8") as fh_out:
-            fc_out = json.load(fh_out)
-            fc_out["data"]["series"] = fc_out["data"]["series"][:-1]
-
-        base_url = "https://docs.google.com/spreadsheets/d"
-        sheet_id = "1WS56qHl9lDK6gjUSfbEVHRmF9mvud1js5SQDcb-mtQs"
-        sheet_name = "sheet1"
-        df = pd.read_csv(
-            f"{base_url}/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}"
-        )
-
-        data = Data()
-        data.add_df(df)
-        self.assertEqual(
-            fc_out,
-            data.build(),
-        )
-
-    def test_add_np_array(self) -> None:
-        with open(self.asset_dir / "df_out.json", encoding="utf8") as fh_out:
-            fc_out = json.load(fh_out)
-            fc_out["data"]["series"] = fc_out["data"]["series"][:-1]
-
-        numpy_array = np.array(
-            [
-                ["Pop", "Hard", 114],
-                ["Rock", "Hard", 96],
-                ["Jazz", "Hard", 78],
-                ["Metal", "Hard", 52],
-                ["Pop", "Smooth", 56],
-                ["Rock", "Experimental", 36],
-                ["Jazz", "Smooth", 174],
-                ["Metal", "Smooth", 121],
-                ["Pop", "Experimental", 127],
-                ["Rock", "Experimental", 83],
-                ["Jazz", "Experimental", 94],
-                ["Metal", "Experimental", 58],
-            ]
-        )
-        data = Data()
-        data.add_np_array(
-            numpy_array,
-            column_name={0: "Genres", 1: "Kinds", 2: "Popularity"},
-            column_dtype={2: int},
-        )
-        self.assertEqual(
-            fc_out,
-            data.build(),
-        )
diff --git a/tests/test_docs/__init__.py b/tests/test_docs/__init__.py
new file mode 100644
index 000000000..735077cde
--- /dev/null
+++ b/tests/test_docs/__init__.py
@@ -0,0 +1 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
diff --git a/tests/test_docs/tutorial/__init__.py b/tests/test_docs/tutorial/__init__.py
new file mode 100644
index 000000000..735077cde
--- /dev/null
+++ b/tests/test_docs/tutorial/__init__.py
@@ -0,0 +1 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
diff --git a/tests/test_docs/tutorial/test_data.py b/tests/test_docs/tutorial/test_data.py
new file mode 100644
index 000000000..edadc0990
--- /dev/null
+++ b/tests/test_docs/tutorial/test_data.py
@@ -0,0 +1,166 @@
+# pylint: disable=missing-module-docstring,missing-class-docstring,missing-function-docstring
+
+from pathlib import Path
+import unittest
+
+import numpy as np
+import pandas as pd
+from pyspark.sql import SparkSession
+from pyspark.sql.types import StructType, StructField, StringType, IntegerType
+
+from ipyvizzu.__version__ import PYENV
+
+from tests.test_data import DataWithAssets
+
+
+class TestDf(DataWithAssets):
+    docs_dir: Path
+
+    df_with_index = pd.DataFrame({"Popularity": [114, 96, 78]}, index=["x", "y", "z"])
+    data_with_index: dict = {
+        "data": {
+            "series": [
+                {
+                    "name": "IndexColumnName",
+                    "type": "dimension",
+                    "values": ["x", "y", "z"],
+                },
+                {"name": "Popularity", "type": "measure", "values": [114, 96, 78]},
+            ]
+        }
+    }
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        super().setUpClass()
+        cls.docs_dir = (
+            Path(__file__).parent.parent.parent.parent / "docs" / "assets" / "data"
+        )
+
+    def test_with_dict(self) -> None:
+        df = pd.DataFrame(self.in_pd_df_by_series)
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series,
+            self.data.build(),
+        )
+
+    def test_with_dict_and_index(self) -> None:
+        df = self.df_with_index
+        self.data.add_df(df, include_index="IndexColumnName")
+        self.assertEqual(
+            self.data_with_index,
+            self.data.build(),
+        )
+
+    def test_index(self) -> None:
+        df = self.df_with_index
+        self.data.add_df_index(df, column_name="IndexColumnName")
+        self.data.add_df(df)
+        self.assertEqual(
+            self.data_with_index,
+            self.data.build(),
+        )
+
+    def test_with_csv(self) -> None:
+        df = pd.read_csv(self.docs_dir / "music_data.csv")
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series,
+            self.data.build(),
+        )
+
+    # TODO: remove decorator once support for Python 3.6 is dropped
+    @unittest.skipUnless(PYENV >= (3, 7), "at least Python 3.7 is required")
+    def test_with_xlsx(self) -> None:
+        df = pd.read_excel(self.docs_dir / "music_data.xlsx")
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series,
+            self.data.build(),
+        )
+
+    def test_with_googlesheet(self) -> None:
+        base_url = "https://docs.google.com/spreadsheets/d"
+        sheet_id = "1WS56qHl9lDK6gjUSfbEVHRmF9mvud1js5SQDcb-mtQs"
+        sheet_name = "sheet1"
+        df = pd.read_csv(
+            f"{base_url}/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}"
+        )
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series,
+            self.data.build(),
+        )
+
+
+class TestNpArray(DataWithAssets):
+    def test_add_np_array(self) -> None:
+        numpy_array = np.array(
+            [
+                ["Pop", "Hard", 114],
+                ["Rock", "Hard", 96],
+                ["Jazz", "Hard", 78],
+                ["Metal", "Hard", 52],
+                ["Pop", "Smooth", 56],
+                ["Rock", "Experimental", 36],
+                ["Jazz", "Smooth", 174],
+                ["Metal", "Smooth", 121],
+                ["Pop", "Experimental", 127],
+                ["Rock", "Experimental", 83],
+                ["Jazz", "Experimental", 94],
+                ["Metal", "Experimental", 58],
+            ]
+        )
+        self.data.add_np_array(
+            numpy_array,
+            column_name={0: "Genres", 1: "Kinds", 2: "Popularity"},
+            column_dtype={2: int},
+        )
+        self.assertEqual(
+            self.ref_pd_df_by_series,
+            self.data.build(),
+        )
+
+
+class TestSparkDf(DataWithAssets):
+    spark: SparkSession
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        super().setUpClass()
+        cls.spark = SparkSession.builder.appName("SparkDocs").getOrCreate()
+
+    @classmethod
+    def tearDownClass(cls) -> None:
+        super().tearDownClass()
+        cls.spark.stop()
+
+    def test_add_df(self) -> None:
+        spark_schema = StructType(
+            [
+                StructField("Genres", StringType(), True),
+                StructField("Kinds", StringType(), True),
+                StructField("Popularity", IntegerType(), True),
+            ]
+        )
+        spark_data = [
+            ("Pop", "Hard", 114),
+            ("Rock", "Hard", 96),
+            ("Jazz", "Hard", 78),
+            ("Metal", "Hard", 52),
+            ("Pop", "Smooth", 56),
+            ("Rock", "Experimental", 36),
+            ("Jazz", "Smooth", 174),
+            ("Metal", "Smooth", 121),
+            ("Pop", "Experimental", 127),
+            ("Rock", "Experimental", 83),
+            ("Jazz", "Experimental", 94),
+            ("Metal", "Experimental", 58),
+        ]
+        df = self.spark.createDataFrame(spark_data, spark_schema)
+        self.data.add_df(df)
+        self.assertEqual(
+            self.ref_pd_df_by_series,
+            self.data.build(),
+        )
diff --git a/tests/test_fugue.py b/tests/test_fugue.py
index 41bd08c05..d72464d40 100644
--- a/tests/test_fugue.py
+++ b/tests/test_fugue.py
@@ -3,27 +3,26 @@
 from contextlib import redirect_stdout
 import io
 import pathlib
-import sys
 import unittest
 
 import pandas as pd
 
+from ipyvizzu.__version__ import PYENV
+
 from tests.utils.normalizer import Normalizer
 
 
-if sys.version_info >= (3, 7):
+# TODO: remove once support for Python 3.6 is dropped
+if PYENV >= (3, 7):
     import fugue.api as fa
     import ipyvizzu.integrations.fugue  # register the extension  # pylint: disable=unused-import
-else:
-    # TODO: remove once support for Python 3.6 is dropped
-    pass
 
 
 class TestFugue(unittest.TestCase):
     # TODO: remove decorator once support for Python 3.6 is dropped
-    @unittest.skipUnless(sys.version_info >= (3, 7), "at least Python 3.7 is required")
+    @unittest.skipUnless(PYENV >= (3, 7), "at least Python 3.7 is required")
     def test_fugue_extension_preset(self) -> None:
-        ref = pathlib.Path(__file__).parent / "assets" / "fugue_preset.txt"
+        ref = pathlib.Path(__file__).parent / "assets" / "ref_fugue_preset.txt"
         with open(ref, "r", encoding="utf8") as f_ref:
             ref_content = f_ref.read()
         df = pd.DataFrame({"a": list("abcde"), "b": range(5)})
@@ -43,9 +42,9 @@ def test_fugue_extension_preset(self) -> None:
         )
 
     # TODO: remove decorator once support for Python 3.6 is dropped
-    @unittest.skipUnless(sys.version_info >= (3, 7), "at least Python 3.7 is required")
+    @unittest.skipUnless(PYENV >= (3, 7), "at least Python 3.7 is required")
     def test_fugue_extension_timeline(self) -> None:
-        ref = pathlib.Path(__file__).parent / "assets" / "fugue_timeline.txt"
+        ref = pathlib.Path(__file__).parent / "assets" / "ref_fugue_timeline.txt"
         with open(ref, "r", encoding="utf8") as f_ref:
             ref_content = f_ref.read()
         df = pd.DataFrame({"a": list("abcde"), "b": range(5), "c": [1, 1, 2, 2, 3]})