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PySpark Bodo Cheatsheet

References of PySpark methods and their Python equivalents supported by Bodo.

pyspark.sql.SparkSession

The table below is a reference of SparkSession methods and their equivalents in Python, which are supported by Bodo.

PySpark Method Python Equivalent
pyspark.sql.SparkSession.read.csv - pd.read_csv()
pyspark.sql.SparkSession.read.text pd.read_csv("file.txt", sep="\n", names=["value"], dtype={"value": "str"})
pyspark.sql.SparkSession.read.parquet pd.read_parquet()
pyspark.sql.SparkSession.read.json pd.read_json()

pyspark.sql.DataFrame

The table below is a reference of Spark DataFrame methods and their equivalents in Python, which are supported by Bodo.

PySpark Method Python Equivalent
pyspark.sql.DataFrame.alias alias = df
pyspark.sql.DataFrame.approxQuantile df[['A', 'B', 'C']].quantile(q)
pyspark.sql.DataFrame.columns df.columns
pyspark.sql.DataFrame.corr df[['A', 'B']].corr()
pyspark.sql.DataFrame.count df.count()
pyspark.sql.DataFrame.cov df[['A', 'B']].cov()
pyspark.sql.DataFrame.crossJoin df1.assign(key=1).merge(df2.assign(key=1), on="key").drop("key", axis=1)
pyspark.sql.DataFrame.describe df.describe()
pyspark.sql.DataFrame.distinct df.distinct()
pyspark.sql.DataFrame.drop df.drop(col, axis=1)
pyspark.sql.DataFrame.dropDuplicates df.drop_duplicates()
pyspark.sql.DataFrame.drop_duplicates df.drop_duplicates()
pyspark.sql.DataFrame.dropna df.dropna()
pyspark.sql.DataFrame.fillna df.fillna(value)
pyspark.sql.DataFrame.filter df[cond]
pyspark.sql.DataFrame.first df.head(1)
pyspark.sql.DataFrame.foreach df.apply(f, axis=1)
pyspark.sql.DataFrame.groupBy df.groupby("col")
pyspark.sql.DataFrame.groupby df.groupby("col")
pyspark.sql.DataFrame.head df.head(n)
pyspark.sql.DataFrame.intersect pd.merge(df1[['col1', 'col2']].drop_duplicates(), df2[['col1', 'col2']].drop_duplicates(), on =['col1', 'col2'])
pyspark.sql.DataFrame.intersectAll pd.merge(df1[['col1', 'col2']], df2[['col1', 'col2']].drop_duplicates(), on =['col1', 'col2'])
pyspark.sql.DataFrame.join df1.join(df2)
pyspark.sql.DataFrame.orderBy df.sort_values('colname')
pyspark.sql.DataFrame.show print(df.head(n))
pyspark.sql.DataFrame.sort df.sort_values('colname')

pyspark.sql.functions

The table below is a reference of Spark SQL functions and their equivalents in Python, which are supported by Bodo.

PySpark Function Python Equivalent
pyspark.sql.functions.abs df.col.abs()
pyspark.sql.functions.acos np.arccos(df.col)
pyspark.sql.functions.acosh np.arccosh(df.col)
pyspark.sql.functions.add_months df.col + pd.DateOffset(months=num_months)
pyspark.sql.functions.approx_count_distinct df.col.nunique()
pyspark.sql.functions.array_contains df.col.apply(lambda a, value: value in a, value=value)
pyspark.sql.functions.array_distinct df.col.map(lambda x: np.unique(x))
pyspark.sql.functions.array_except df[['col1', 'col2']].apply(lambda x: np.setdiff1d(x[0], x[1]), axis=1)
pyspark.sql.functions.array_join df.col.apply(lambda x, sep: sep.join(x), sep=sep)
pyspark.sql.functions.array_max df.col.map(lambda x: np.nanmax(x))
pyspark.sql.functions.array_min df.col.map(lambda x: np.nanmin(x))
pyspark.sql.functions.array_position df.col.apply(lambda x, value: np.append(np.where(x == value)[0], -1)[0], value=value)
pyspark.sql.functions.array_repeat df.col.apply(lambda x, count: np.repeat(x, count), count=count)
pyspark.sql.functions.array_sort df.col.map(lambda x: np.sort(x))
pyspark.sql.functions.array_union df[['col1', 'col2']].apply(lambda x: np.union1d(x[0], x[1]), axis=1)
pyspark.sql.functions.array_overlap df[['A', 'B']].apply(lambda x: len(np.intersect1d(x[0], x[1])) > 0, axis=1)
pyspark.sql.functions.asc df.sort_values('col')
pyspark.sql.functions.asc_nulls_first df.sort_values('col', na_position='first')
pyspark.sql.functions.asc_nulls_last df.sort_values('col')
pyspark.sql.functions.ascii df.col.map(lambda x: ord(x[0]))
pyspark.sql.functions.asin np.arcsin(df.col)
pyspark.sql.functions.asinh np.arcsinh(df.col)
pyspark.sql.functions.atan np.arctan(df.col)
pyspark.sql.functions.atanh np.arctanh(df.col)
pyspark.sql.functions.atan2 df[['col1', 'col2']].apply(lambda x: np.arctan2(x[0], x[1]), axis=1)
pyspark.sql.functions.avg df.col.mean()
pyspark.sql.functions.bin df.col.map(lambda x: "{0:b}".format(x))
pyspark.sql.functions.bitwiseNOT np.invert(df.col)
pyspark.sql.functions.bround df.col.apply(lambda x, scale: np.round(x, scale), scale=scale)
pyspark.sql.functions.cbrt df.col.map(lambda x: np.cbrt(x))
pyspark.sql.functions.ceil np.ceil(df.col)
pyspark.sql.functions.col df.col
pyspark.sql.functions.collect_list df.col.to_numpy()
pyspark.sql.functions.collect_set np.unique(df.col.to_numpy())
pyspark.sql.functions.column df.col
pyspark.sql.functions.corr df[['col1', 'col2']].corr(method = 'pearson')
pyspark.sql.functions.cos np.cos(df.col)
pyspark.sql.functions.cosh np.cosh(df.col)
pyspark.sql.functions.count df.col.count()
pyspark.sql.functions.countDistinct df.col.drop_duplicates().count()
pyspark.sql.functions.current_date datetime.datetime.now().date()
pyspark.sql.functions.current_timestamp datetime.datetime.now()
pyspark.sql.functions.date_add df.col + pd.tseries.offsets.DateOffset(num_days)
pyspark.sql.functions.date_format df.col.dt.strftime(format_str)
pyspark.sql.functions.date_sub df.col - pd.tseries.offsets.DateOffset(num_days)
pyspark.sql.functions.datediff (df.col1 - df.col2).dt.days
pyspark.sql.functions.dayofmonth df.col.dt.day
pyspark.sql.functions.dayofweek df.col.dt.dayofweek
pyspark.sql.functions.dayofyear df.col.dt.dayofyear
pyspark.sql.functions.degrees np.degrees(df.col)
pyspark.sql.functions.desc df.sort_values('col', ascending=False)
pyspark.sql.functions.desc_nulls_first df.sort_values('col', ascending=False, na_position='first')
pyspark.sql.functions.desc_nulls_last df.sort_values('col', ascending=False)
pyspark.sql.functions.exp np.exp(df.col)
pyspark.sql.functions.expm1 np.exp(df.col) - 1
pyspark.sql.functions.factorial df.col.map(lambda x: math.factorial(x))
pyspark.sql.functions.filter df.filter()
pyspark.sql.functions.floor np.floor(df.col)
pyspark.sql.functions.format_number df.col.apply(lambda x,d : ("{:,." + str(d) + "f}").format(np.round(x, d)), d=d)
pyspark.sql.functions.format_string df.col.apply(lambda x, format_str : format_str.format(x), format_str=format_str)
pyspark.sql.functions.from_unixtime df.col.map(lambda x: pd.Timestamp(x, 's')).dt.strftime(format_str)
pyspark.sql.functions.greatest df[['col1', 'col2']].apply(lambda x: np.nanmax(x), axis=1)
pyspark.sql.functions.hash df.col.map(lambda x: hash(x))
pyspark.sql.functions.hour df.col.dt.hour
pyspark.sql.functions.hypot df[['col1', 'col2']].apply(lambda x: np.hypot(x[0], x[1]), axis=1)
pyspark.sql.functions.initcap df.col.str.title()
pyspark.sql.functions.instr df.col.str.find(sub=substr)
pyspark.sql.functions.isnan np.isnan(df.col)
pyspark.sql.functions.isnull df.col.isna()
pyspark.sql.functions.kurtosis df.col.kurtosis()
pyspark.sql.functions.last_day df.col + pd.tseries.offsets.MonthEnd()
pyspark.sql.functions.least df.min(axis=1)
pyspark.sql.functions.locate df.col.str.find(sub=substr, start=start)
pyspark.sql.functions.log np.log(df.col) / np.log(base)
pyspark.sql.functions.log10 np.log10(df.col)
pyspark.sql.functions.log1p np.log(df.col) + 1
pyspark.sql.functions.log2 np.log2(df.col)
pyspark.sql.functions.lower df.col.str.lower()
pyspark.sql.functions.lpad df.col.str.pad(len, flllchar=char)
pyspark.sql.functions.ltrim df.col.str.lstrip()
pyspark.sql.functions.max df.col.max()
pyspark.sql.functions.mean df.col.mean()
pyspark.sql.functions.min df.col.min()
pyspark.sql.functions.minute df.col.dt.minute
pyspark.sql.functions.monotonically_increasing_id pd.Series(np.arange(len(df)))
pyspark.sql.functions.month df.col.dt.month
pyspark.sql.functions.nanvl df[['A', 'B']].apply(lambda x: x[0] if not pd.isna(x[0]) else x[1], axis=1)
pyspark.sql.functions.overlay df.A.str.slice_replace(start=index, stop=index+len, repl=repl_str)
pyspark.sql.functions.pandas_udf df.apply(f) or df.col.map(f)
pyspark.sql.functions.pow np.power(df.col1, df.col2)
pyspark.sql.functions.quarter df.col.dt.quarter
pyspark.sql.functions.radians np.radians(df.col)
pyspark.sql.functions.rand pd.Series(np.random.rand(1, num_cols))
pyspark.sql.functions.randn pd.Series(np.random.randn(num_cols))
pyspark.sql.functions.regexp_replace df.col.str.replace(pattern, repl_string)
pyspark.sql.functions.repeat df.col.str.repeat(count)
pyspark.sql.functions.reverse df.col.map(lambda x: x[::-1])
pyspark.sql.functions.rint df.col.map(lambda x: int(np.round(x, 0)))
pyspark.sql.functions.round df.col.apply(lambda x, decimal_places: np.round(x, decimal_places), decimal_places=decimal_places)
pyspark.sql.functions.rpad df.col.str.pad(len, side='right', flllchar=char)
pyspark.sql.functions.rtrim df.col.str.rstrip()
pyspark.sql.functions.second df.col.dt.second
pyspark.sql.functions.sequence df[['col1', 'col2', 'col3']].apply(lambda x: np.arange(x[0], x[1], x[2]), axis=1)
pyspark.sql.functions.shuffle df.col.map(lambda x: np.random.permutation(x))
pyspark.sql.functions.signum np.sign(df.col)
pyspark.sql.functions.sin np.sin(df.col)
pyspark.sql.functions.sinh np.sinh(df.col)
pyspark.sql.functions.size df.col.map(lambda x: len(x))
pyspark.sql.functions.skewness df.col.skew()
pyspark.sql.functions.slice df.col.map(lambda x: x[start : end])
pyspark.sql.functions.split df.col.str.split(pat, num_splits)
pyspark.sql.functions.sqrt np.sqrt(df.col)
pyspark.sql.functions.stddev df.col.std()
pyspark.sql.functions.stddev_pop df.col.std(ddof=0)
pyspark.sql.functions.stddev_samp df.col.std()
pyspark.sql.functions.substring df.col.str.slice(start, start+len)
pyspark.sql.functions.substring_index df.col.apply(lambda x, sep, count: sep.join(x.split(sep)[:count]), sep=sep, count=count)
pyspark.sql.functions.sum df.col.sum()
pyspark.sql.functions.sumDistinct df.col.drop_duplicates().sum()
pyspark.sql.functions.tan np.tan(df.col)
pyspark.sql.functions.tanh np.tanh(df.col)
pyspark.sql.functions.timestamp_seconds pd.to_datetime("now")
pyspark.sql.functions.to_date df.col.apply(lambda x, format_str: pd.to_datetime(x, format=format_str).date(), format_str=format_str)
pyspark.sql.functions.to_timestamp df.A.apply(lambda x, format_str: pd.to_datetime(x, format=format_str), format_str=format_str)
pyspark.sql.functions.translate df.col.str.split("").apply(lambda x: "".join(pd.Series(x).replace(to_replace, values).tolist()), to_replace=to_replace, values=values)
pyspark.sql.functions.trim df.col.str.strip()
pyspark.sql.functions.udf df.apply or df.col.map
pyspark.sql.functions.unix_timestamp df.col.apply(lambda x, format_str: (pd.to_datetime(x, format=format_str) - pd.Timestamp("1970-01-01")).total_seconds(), format_str=format_str)
pyspark.sql.functions.upper df.col.str.upper()
pyspark.sql.functions.var_pop df.col.var(ddof=0)
pyspark.sql.functions.var_samp df.col.var()
pyspark.sql.functions.variance df.col.var()
pyspark.sql.functions.weekofyear df.col.dt.isocalendar().week
pyspark.sql.functions.when df.A.apply(lambda a, cond, val, other: val if cond(a) else other, cond=cond, val=val, other=other)
pyspark.sql.functions.year df.col.dt.year

Special Cases

pyspark.sql.functions.concat

  • pyspark.sql.functions.concat
    • for Arrays : df[['col1', 'col2', 'col3']].apply(lambda x: np.hstack(x), axis=1)
    • for Strings : df[['col1', 'col2', 'col3']].apply(lambda x: "".join(x), axis=1)

pyspark.sql.functions.conv

  • pyspark.sql.functions.conv

    pandas equivalent: 

    base_map = {2: "{0:b}", 8: "{0:o}", 10: "{0:d}", 16: "{0:x}"}
new_format = base_map[new_base]
df.col.apply(lambda x, old_base, new_format: new_format.format(int(x, old_base)), old_base=old_base, new_format=new_format)

pyspark.sql.functions.date_trunc

  • pyspark.sql.functions.date_trunc

    • For frequencies day and below: df.col.dt.floor(freq=trunc_val)
    • For month: df.col.map(lambda x: pd.Timestamp(year=x.year, month=x.month, day=1))
    • For year: df.col.map(lambda x: pd.Timestamp(year=x.year, month=1, day=1))

pyspark.sql.functions.regexp_extract

  • pyspark.sql.functions.regexp_extract

    Here's a small pandas function equivalent:

    def f(x, pat):
    res = re.search(pat, x)
    return "" if res is None else res[0]
df.col.apply(f, pat=pat)

pyspark.sql.functions.shiftLeft

  • pyspark.sql.functions.shiftLeft

    • If the type is uint64 np.left_shift(df.col.astype(np.int64), numbits).astype(np.uint64))
    • Other integer types: np.left_shift(df.col, numbits)

pyspark.sql.functions.shiftRight

  • pyspark.sql.functions.shiftRight

    • If the type is uint64 use shiftRightUnsigned
    • Other integer types: np.right_shift(df.col, numbits)

pyspark.sql.functions.shiftRightUnsigned

  • pyspark.sql.functions.shiftRightUnsigned

    Here's a small pandas function equivalent:

    def shiftRightUnsigned(col, num_bits):
    bits_minus_1 = max((num_bits - 1), 0)
    mask_bits = (np.int64(1) << bits_minus_1) - 1
    mask = ~(mask_bits << (63 - bits_minus_1))
    return np.right_shift(col.astype(np.int64), num_bits) & mask).astype(np.uint64)
shiftRightUnsigned(df.col, numbits)

pyspark.sql.functions.sort_array

  • pyspark.sql.functions.sort_array

    • Ascending: df.col.map(lambda x: np.sort(x))
    • Descending: df.col.map(lambda x: np.sort(x)[::-1])

pyspark.sql.functions.trunc

  • pyspark.sql.functions.trunc

    def f(date, trunc_str):
    if trunc_str == 'year':
        return pd.Timestamp(year=date.year, month=1, day=1)
    if trunc_str == 'month':
        return pd.Timestamp(year=date.year, month=date.month, day=1)
df.A.apply(f, trunc_str=trunc_str)
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