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| NGram |
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| OneHotEncoder |
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| OneHotEncoderModel |
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| PCA |
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| PolynomialExpansion |
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| QuantileDiscretizer |
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| RobustScaler |
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| RegexTokenizer |
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| SQLTransformer |
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| StandardScaler |
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| StandardScalerModel |
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| StopWordsRemover |
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| StringIndexer |
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| StringIndexerModel |
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| Tokenizer |
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| UnivariateFeatureSelector |
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| <a class="reference internal" href="../../api/pyspark.ml.feature.UnivariateFeatureSelectorModel.html"> |
| UnivariateFeatureSelectorModel |
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| VarianceThresholdSelector |
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| VarianceThresholdSelectorModel |
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| VectorAssembler |
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| VectorIndexer |
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| VectorIndexerModel |
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| VectorSizeHint |
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| VectorSlicer |
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| Word2Vec |
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| <a class="reference internal" href="../../api/pyspark.ml.feature.Word2VecModel.html"> |
| Word2VecModel |
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| LinearSVC |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.LinearSVCModel.html"> |
| LinearSVCModel |
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| LinearSVCSummary |
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| LinearSVCTrainingSummary |
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| LogisticRegression |
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| LogisticRegressionModel |
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| LogisticRegressionSummary |
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| LogisticRegressionTrainingSummary |
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| BinaryLogisticRegressionSummary |
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| BinaryLogisticRegressionTrainingSummary |
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| DecisionTreeClassifier |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.DecisionTreeClassificationModel.html"> |
| DecisionTreeClassificationModel |
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| GBTClassifier |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.GBTClassificationModel.html"> |
| GBTClassificationModel |
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| RandomForestClassifier |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.RandomForestClassificationModel.html"> |
| RandomForestClassificationModel |
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| RandomForestClassificationSummary |
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| RandomForestClassificationTrainingSummary |
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| BinaryRandomForestClassificationSummary |
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| BinaryRandomForestClassificationTrainingSummary |
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| NaiveBayes |
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| NaiveBayesModel |
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| MultilayerPerceptronClassifier |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.MultilayerPerceptronClassificationModel.html"> |
| MultilayerPerceptronClassificationModel |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.MultilayerPerceptronClassificationSummary.html"> |
| MultilayerPerceptronClassificationSummary |
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| MultilayerPerceptronClassificationTrainingSummary |
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| OneVsRest |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.OneVsRestModel.html"> |
| OneVsRestModel |
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| FMClassifier |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.FMClassificationModel.html"> |
| FMClassificationModel |
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| <a class="reference internal" href="../../api/pyspark.ml.classification.FMClassificationSummary.html"> |
| FMClassificationSummary |
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| FMClassificationTrainingSummary |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.BisectingKMeans.html"> |
| BisectingKMeans |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.BisectingKMeansModel.html"> |
| BisectingKMeansModel |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.BisectingKMeansSummary.html"> |
| BisectingKMeansSummary |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.KMeans.html"> |
| KMeans |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.KMeansModel.html"> |
| KMeansModel |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.KMeansSummary.html"> |
| KMeansSummary |
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| GaussianMixture |
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| <li class="toctree-l2"> |
| <a class="reference internal" href="../../api/pyspark.ml.clustering.GaussianMixtureModel.html"> |
| GaussianMixtureModel |
| </a> |
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| <li class="toctree-l2"> |
| <a class="reference internal" href="../../api/pyspark.ml.clustering.GaussianMixtureSummary.html"> |
| GaussianMixtureSummary |
| </a> |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.LDA.html"> |
| LDA |
| </a> |
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| <li class="toctree-l2"> |
| <a class="reference internal" href="../../api/pyspark.ml.clustering.LDAModel.html"> |
| LDAModel |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.LocalLDAModel.html"> |
| LocalLDAModel |
| </a> |
| </li> |
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| <a class="reference internal" href="../../api/pyspark.ml.clustering.DistributedLDAModel.html"> |
| DistributedLDAModel |
| </a> |
| </li> |
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| PowerIterationClustering |
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| pyspark.ml.functions.array_to_vector |
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| <a class="reference internal" href="../../api/pyspark.ml.functions.vector_to_array.html"> |
| pyspark.ml.functions.vector_to_array |
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| <a class="reference internal" href="../../api/pyspark.ml.functions.predict_batch_udf.html"> |
| pyspark.ml.functions.predict_batch_udf |
| </a> |
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| Vector |
| </a> |
| </li> |
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| DenseVector |
| </a> |
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| <a class="reference internal" href="../../api/pyspark.ml.linalg.SparseVector.html"> |
| SparseVector |
| </a> |
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| <a class="reference internal" href="../../api/pyspark.ml.linalg.Vectors.html"> |
| Vectors |
| </a> |
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| Matrix |
| </a> |
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| DenseMatrix |
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| SparseMatrix |
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| Matrices |
| </a> |
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| ALS |
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| ALSModel |
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| AFTSurvivalRegression |
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| AFTSurvivalRegressionModel |
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| DecisionTreeRegressor |
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| DecisionTreeRegressionModel |
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| <div class="section" id="pyspark-pandas-read-excel"> |
| <h1>pyspark.pandas.read_excel<a class="headerlink" href="#pyspark-pandas-read-excel" title="Permalink to this headline">¶</a></h1> |
| <dl class="py function"> |
| <dt id="pyspark.pandas.read_excel"> |
| <code class="sig-prename descclassname">pyspark.pandas.</code><code class="sig-name descname">read_excel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">io</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>str<span class="p">, </span>Any<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">sheet_name</span><span class="p">:</span> <span class="n">Union[str, int, List[Union[str, int]], None]</span> <span class="o">=</span> <span class="default_value">0</span></em>, <em class="sig-param"><span class="n">header</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>int<span class="p">, </span>List<span class="p">[</span>int<span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">0</span></em>, <em class="sig-param"><span class="n">names</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>List<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">index_col</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>List<span class="p">[</span>int<span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">usecols</span><span class="p">:</span> <span class="n">Union[int, str, List[Union[str, int]], Callable[[str], bool], None]</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">squeeze</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em>, <em class="sig-param"><span class="n">dtype</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Dict<span class="p">[</span>str<span class="p">, </span>Union<span class="p">[</span>str<span class="p">, </span>numpy.dtype<span class="p">, </span>pandas.core.dtypes.base.ExtensionDtype<span class="p">]</span><span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">engine</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>str<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">converters</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Dict<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">true_values</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Any<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">false_values</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Any<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">skiprows</span><span class="p">:</span> <span class="n">Union[int, List[int], None]</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">nrows</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>int<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">na_values</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Any<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">keep_default_na</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">True</span></em>, <em class="sig-param"><span class="n">verbose</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">False</span></em>, <em class="sig-param"><span class="n">parse_dates</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>bool<span class="p">, </span>List<span class="p">, </span>Dict<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">False</span></em>, <em class="sig-param"><span class="n">date_parser</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Callable<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">thousands</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>str<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">comment</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>str<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">skipfooter</span><span class="p">:</span> <span class="n">int</span> <span class="o">=</span> <span class="default_value">0</span></em>, <em class="sig-param"><span class="n">convert_float</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">True</span></em>, <em class="sig-param"><span class="n">mangle_dupe_cols</span><span class="p">:</span> <span class="n">bool</span> <span class="o">=</span> <span class="default_value">True</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwds</span><span class="p">:</span> <span class="n">Any</span></em><span class="sig-paren">)</span> → Union<span class="p">[</span>pyspark.pandas.frame.DataFrame<span class="p">, </span>pyspark.pandas.series.Series<span class="p">, </span>Dict<span class="p">[</span>str<span class="p">, </span>Union<span class="p">[</span>pyspark.pandas.frame.DataFrame<span class="p">, </span>pyspark.pandas.series.Series<span class="p">]</span><span class="p">]</span><span class="p">]</span><a class="reference internal" href="../../../_modules/pyspark/pandas/namespace.html#read_excel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyspark.pandas.read_excel" title="Permalink to this definition">¶</a></dt> |
| <dd><p>Read an Excel file into a pandas-on-Spark DataFrame or Series.</p> |
| <p>Support both <cite>xls</cite> and <cite>xlsx</cite> file extensions from a local filesystem or URL. |
| Support an option to read a single sheet or a list of sheets.</p> |
| <dl class="field-list"> |
| <dt class="field-odd">Parameters</dt> |
| <dd class="field-odd"><dl> |
| <dt><strong>io</strong><span class="classifier">str, file descriptor, pathlib.Path, ExcelFile or xlrd.Book</span></dt><dd><p>The string could be a URL. The value URL must be available in Spark’s DataFrameReader.</p> |
| <div class="admonition note"> |
| <p class="admonition-title">Note</p> |
| <p>If the underlying Spark is below 3.0, the parameter as a string is not supported. |
| You can use <cite>ps.from_pandas(pd.read_excel(…))</cite> as a workaround.</p> |
| </div> |
| </dd> |
| <dt><strong>sheet_name</strong><span class="classifier">str, int, list, or None, default 0</span></dt><dd><p>Strings are used for sheet names. Integers are used in zero-indexed |
| sheet positions. Lists of strings/integers are used to request |
| multiple sheets. Specify None to get all sheets.</p> |
| <p>Available cases:</p> |
| <ul class="simple"> |
| <li><p>Defaults to <code class="docutils literal notranslate"><span class="pre">0</span></code>: 1st sheet as a <cite>DataFrame</cite></p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">1</span></code>: 2nd sheet as a <cite>DataFrame</cite></p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">"Sheet1"</span></code>: Load sheet with name “Sheet1”</p></li> |
| <li><p><code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1,</span> <span class="pre">"Sheet5"]</span></code>: Load first, second and sheet named “Sheet5” |
| as a dict of <cite>DataFrame</cite></p></li> |
| <li><p>None: All sheets.</p></li> |
| </ul> |
| </dd> |
| <dt><strong>header</strong><span class="classifier">int, list of int, default 0</span></dt><dd><p>Row (0-indexed) to use for the column labels of the parsed |
| DataFrame. If a list of integers is passed those row positions will |
| be combined into a <code class="docutils literal notranslate"><span class="pre">MultiIndex</span></code>. Use None if there is no header.</p> |
| </dd> |
| <dt><strong>names</strong><span class="classifier">array-like, default None</span></dt><dd><p>List of column names to use. If file contains no header row, |
| then you should explicitly pass header=None.</p> |
| </dd> |
| <dt><strong>index_col</strong><span class="classifier">int, list of int, default None</span></dt><dd><p>Column (0-indexed) to use as the row labels of the DataFrame. |
| Pass None if there is no such column. If a list is passed, |
| those columns will be combined into a <code class="docutils literal notranslate"><span class="pre">MultiIndex</span></code>. If a |
| subset of data is selected with <code class="docutils literal notranslate"><span class="pre">usecols</span></code>, index_col |
| is based on the subset.</p> |
| </dd> |
| <dt><strong>usecols</strong><span class="classifier">int, str, list-like, or callable default None</span></dt><dd><p>Return a subset of the columns.</p> |
| <ul class="simple"> |
| <li><p>If None, then parse all columns.</p></li> |
| <li><p>If str, then indicates comma separated list of Excel column letters |
| and column ranges (e.g. “A:E” or “A,C,E:F”). Ranges are inclusive of |
| both sides.</p></li> |
| <li><p>If list of int, then indicates list of column numbers to be parsed.</p></li> |
| <li><p>If list of string, then indicates list of column names to be parsed.</p></li> |
| <li><p>If callable, then evaluate each column name against it and parse the |
| column if the callable returns <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li> |
| </ul> |
| </dd> |
| <dt><strong>squeeze</strong><span class="classifier">bool, default False</span></dt><dd><p>If the parsed data only contains one column then return a Series.</p> |
| <div class="deprecated"> |
| <p><span class="versionmodified deprecated">Deprecated since version 3.4.0.</span></p> |
| </div> |
| </dd> |
| <dt><strong>dtype</strong><span class="classifier">Type name or dict of column -> type, default None</span></dt><dd><p>Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} |
| Use <cite>object</cite> to preserve data as stored in Excel and not interpret dtype. |
| If converters are specified, they will be applied INSTEAD |
| of dtype conversion.</p> |
| </dd> |
| <dt><strong>engine</strong><span class="classifier">str, default None</span></dt><dd><p>If io is not a buffer or path, this must be set to identify io. |
| Acceptable values are None or xlrd.</p> |
| </dd> |
| <dt><strong>converters</strong><span class="classifier">dict, default None</span></dt><dd><p>Dict of functions for converting values in certain columns. Keys can |
| either be integers or column labels, values are functions that take one |
| input argument, the Excel cell content, and return the transformed |
| content.</p> |
| </dd> |
| <dt><strong>true_values</strong><span class="classifier">list, default None</span></dt><dd><p>Values to consider as True.</p> |
| </dd> |
| <dt><strong>false_values</strong><span class="classifier">list, default None</span></dt><dd><p>Values to consider as False.</p> |
| </dd> |
| <dt><strong>skiprows</strong><span class="classifier">list-like</span></dt><dd><p>Rows to skip at the beginning (0-indexed).</p> |
| </dd> |
| <dt><strong>nrows</strong><span class="classifier">int, default None</span></dt><dd><p>Number of rows to parse.</p> |
| </dd> |
| <dt><strong>na_values</strong><span class="classifier">scalar, str, list-like, or dict, default None</span></dt><dd><p>Additional strings to recognize as NA/NaN. If dict passed, specific |
| per-column NA values. By default the following values are interpreted |
| as NaN.</p> |
| </dd> |
| <dt><strong>keep_default_na</strong><span class="classifier">bool, default True</span></dt><dd><p>If na_values are specified and keep_default_na is False the default NaN |
| values are overridden, otherwise they’re appended to.</p> |
| </dd> |
| <dt><strong>verbose</strong><span class="classifier">bool, default False</span></dt><dd><p>Indicate number of NA values placed in non-numeric columns.</p> |
| </dd> |
| <dt><strong>parse_dates</strong><span class="classifier">bool, list-like, or dict, default False</span></dt><dd><p>The behavior is as follows:</p> |
| <ul class="simple"> |
| <li><p>bool. If True -> try parsing the index.</p></li> |
| <li><p>list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3 |
| each as a separate date column.</p></li> |
| <li><p>list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as |
| a single date column.</p></li> |
| <li><p>dict, e.g. {{‘foo’ : [1, 3]}} -> parse columns 1, 3 as date and call |
| result ‘foo’</p></li> |
| </ul> |
| <p>If a column or index contains an unparseable date, the entire column or |
| index will be returned unaltered as an object data type. For non-standard |
| datetime parsing, use <code class="docutils literal notranslate"><span class="pre">pd.to_datetime</span></code> after <code class="docutils literal notranslate"><span class="pre">pd.read_csv</span></code></p> |
| <p>Note: A fast-path exists for iso8601-formatted dates.</p> |
| </dd> |
| <dt><strong>date_parser</strong><span class="classifier">function, optional</span></dt><dd><p>Function to use for converting a sequence of string columns to an array of |
| datetime instances. The default uses <code class="docutils literal notranslate"><span class="pre">dateutil.parser.parser</span></code> to do the |
| conversion. pandas-on-Spark will try to call <cite>date_parser</cite> in three different ways, |
| advancing to the next if an exception occurs: 1) Pass one or more arrays |
| (as defined by <cite>parse_dates</cite>) as arguments; 2) concatenate (row-wise) the |
| string values from the columns defined by <cite>parse_dates</cite> into a single array |
| and pass that; and 3) call <cite>date_parser</cite> once for each row using one or |
| more strings (corresponding to the columns defined by <cite>parse_dates</cite>) as |
| arguments.</p> |
| </dd> |
| <dt><strong>thousands</strong><span class="classifier">str, default None</span></dt><dd><p>Thousands separator for parsing string columns to numeric. Note that |
| this parameter is only necessary for columns stored as TEXT in Excel, |
| any numeric columns will automatically be parsed, regardless of display |
| format.</p> |
| </dd> |
| <dt><strong>comment</strong><span class="classifier">str, default None</span></dt><dd><p>Comments out remainder of line. Pass a character or characters to this |
| argument to indicate comments in the input file. Any data between the |
| comment string and the end of the current line is ignored.</p> |
| </dd> |
| <dt><strong>skipfooter</strong><span class="classifier">int, default 0</span></dt><dd><p>Rows at the end to skip (0-indexed).</p> |
| </dd> |
| <dt><strong>convert_float</strong><span class="classifier">bool, default True</span></dt><dd><p>Convert integral floats to int (i.e., 1.0 –> 1). If False, all numeric |
| data will be read in as floats: Excel stores all numbers as floats |
| internally.</p> |
| <div class="deprecated"> |
| <p><span class="versionmodified deprecated">Deprecated since version 3.4.0.</span></p> |
| </div> |
| </dd> |
| <dt><strong>mangle_dupe_cols</strong><span class="classifier">bool, default True</span></dt><dd><p>Duplicate columns will be specified as ‘X’, ‘X.1’, …’X.N’, rather than |
| ‘X’…’X’. Passing in False will cause data to be overwritten if there |
| are duplicate names in the columns.</p> |
| <div class="deprecated"> |
| <p><span class="versionmodified deprecated">Deprecated since version 3.4.0.</span></p> |
| </div> |
| </dd> |
| <dt><strong>**kwds</strong><span class="classifier">optional</span></dt><dd><p>Optional keyword arguments can be passed to <code class="docutils literal notranslate"><span class="pre">TextFileReader</span></code>.</p> |
| </dd> |
| </dl> |
| </dd> |
| <dt class="field-even">Returns</dt> |
| <dd class="field-even"><dl class="simple"> |
| <dt>DataFrame or dict of DataFrames</dt><dd><p>DataFrame from the passed in Excel file. See notes in sheet_name |
| argument for more information on when a dict of DataFrames is returned.</p> |
| </dd> |
| </dl> |
| </dd> |
| </dl> |
| <div class="admonition seealso"> |
| <p class="admonition-title">See also</p> |
| <dl class="simple"> |
| <dt><a class="reference internal" href="pyspark.pandas.DataFrame.to_excel.html#pyspark.pandas.DataFrame.to_excel" title="pyspark.pandas.DataFrame.to_excel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame.to_excel</span></code></a></dt><dd><p>Write DataFrame to an Excel file.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.DataFrame.to_csv.html#pyspark.pandas.DataFrame.to_csv" title="pyspark.pandas.DataFrame.to_csv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DataFrame.to_csv</span></code></a></dt><dd><p>Write DataFrame to a comma-separated values (csv) file.</p> |
| </dd> |
| <dt><a class="reference internal" href="pyspark.pandas.read_csv.html#pyspark.pandas.read_csv" title="pyspark.pandas.read_csv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read_csv</span></code></a></dt><dd><p>Read a comma-separated values (csv) file into DataFrame.</p> |
| </dd> |
| </dl> |
| </div> |
| <p class="rubric">Examples</p> |
| <p>The file can be read using the file name as string or an open file object:</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ps</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="s1">'tmp.xlsx'</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> |
| <span class="go"> Name Value</span> |
| <span class="go">0 string1 1</span> |
| <span class="go">1 string2 2</span> |
| <span class="go">2 #Comment 3</span> |
| </pre></div> |
| </div> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ps</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">'tmp.xlsx'</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">),</span> |
| <span class="gp">... </span> <span class="n">sheet_name</span><span class="o">=</span><span class="s1">'Sheet3'</span><span class="p">)</span> |
| <span class="go"> Unnamed: 0 Name Value</span> |
| <span class="go">0 0 string1 1</span> |
| <span class="go">1 1 string2 2</span> |
| <span class="go">2 2 #Comment 3</span> |
| </pre></div> |
| </div> |
| <p>Index and header can be specified via the <cite>index_col</cite> and <cite>header</cite> arguments</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ps</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="s1">'tmp.xlsx'</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> |
| <span class="go"> 0 1 2</span> |
| <span class="go">0 NaN Name Value</span> |
| <span class="go">1 0.0 string1 1</span> |
| <span class="go">2 1.0 string2 2</span> |
| <span class="go">3 2.0 #Comment 3</span> |
| </pre></div> |
| </div> |
| <p>Column types are inferred but can be explicitly specified</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ps</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="s1">'tmp.xlsx'</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">dtype</span><span class="o">=</span><span class="p">{</span><span class="s1">'Name'</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="s1">'Value'</span><span class="p">:</span> <span class="nb">float</span><span class="p">})</span> |
| <span class="go"> Name Value</span> |
| <span class="go">0 string1 1.0</span> |
| <span class="go">1 string2 2.0</span> |
| <span class="go">2 #Comment 3.0</span> |
| </pre></div> |
| </div> |
| <p>True, False, and NA values, and thousands separators have defaults, |
| but can be explicitly specified, too. Supply the values you would like |
| as strings or lists of strings!</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ps</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="s1">'tmp.xlsx'</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> |
| <span class="gp">... </span> <span class="n">na_values</span><span class="o">=</span><span class="p">[</span><span class="s1">'string1'</span><span class="p">,</span> <span class="s1">'string2'</span><span class="p">])</span> |
| <span class="go"> Name Value</span> |
| <span class="go">0 None 1</span> |
| <span class="go">1 None 2</span> |
| <span class="go">2 #Comment 3</span> |
| </pre></div> |
| </div> |
| <p>Comment lines in the excel input file can be skipped using the <cite>comment</cite> kwarg</p> |
| <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ps</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="s1">'tmp.xlsx'</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">comment</span><span class="o">=</span><span class="s1">'#'</span><span class="p">)</span> |
| <span class="go"> Name Value</span> |
| <span class="go">0 string1 1.0</span> |
| <span class="go">1 string2 2.0</span> |
| <span class="go">2 None NaN</span> |
| </pre></div> |
| </div> |
| </dd></dl> |
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