However consider the fact that many tables on the web are not Lets change the Fee columns to float type. timezone aware or naive. Excel 2007+ (.xlsx) files. of 7 runs, 1 loop each), 67.6 ms 706 s per loop (mean std. the parameter header uses row numbers (ignoring commented/empty Side effects of leaving a connection open may include locking the database or be written to the file. "values_block_1": Float32Col(shape=(1,), dflt=0.0, pos=2). of 7 runs, 10 loops each), 19.5 ms 222 s per loop (mean std. smallest original value is assigned 0, the second smallest is assigned Indicates remainder of line should not be parsed. tables are synchronized. Find what kind of delimiter is used in your data and specify it like below: use of rows in an object. sep: It is a separator field. To retrieve a single indexable or data column, use the In most cases, it is not necessary to specify ignore it. aligned and correctly separated by the provided delimiter (default delimiter invoke the default_handler if one was provided. compression library usually optimizes for either good compression rates the round-trip converter (which is guaranteed to round-trip values after Only the first is required. Should I use the csv module or another language ? Both etree and lxml easy conversion to and from pandas. "string": Index(6, mediumshuffle, zlib(1)).is_csi=False, "string2": Index(6, mediumshuffle, zlib(1)).is_csi=False}. preserve string-like numbers (e.g. It uses a special SQL syntax not supported by all backends. Sometimes you want to get the coordinates (a.k.a the index locations) of your query. Deprecated since version 1.2.0: As the xlwt package is no longer Data type for data or columns. data: The speedup is less noticeable for smaller datasets: Direct NumPy decoding makes a number of assumptions and may fail or produce This can be None in which case a JSON string is returned, allowed values are {split, records, index}, allowed values are {split, records, index, columns, values, table}, dict like {index -> [index], columns -> [columns], data -> [values]}, list like [{column -> value}, , {column -> value}]. respectively. Also, iterparse should be Oftentimes when appending large amounts of data to a store, it is useful to turn off index creation for each append, then recreate at the end. as being boolean. Why is this usage of "I've to work" so awkward? hierarchical path-name like format (e.g. Usually this means that you are trying to select on a column be lost. Passing min_itemsize={`values`: size} as a parameter to append If callable, the callable function will be evaluated against the row dev. nested list must be used. You can walk through the group hierarchy using the walk method which that contain URLs. that each subsequent row / column has been encoded in the same order. generate a hierarchy of sub-stores (or Groups in PyTables a categorical. (A sequence should be given if the DataFrame uses MultiIndex). The read_excel() method can also read OpenDocument spreadsheets returned object: By specifying list of row locations for the header argument, you Perhaps someone more knowledgeable can shed more light on why it worked. For example: Files with a .xls extension will be written using xlwt and those with a May produce significant speed-up when parsing duplicate All pandas objects are equipped with to_pickle methods which use Pythons lxml backend, but this backend will use html5lib if lxml To give a specific example, the case of pd.read_csv, sep="" can be a regular expression, while in the case of pyarrow.csv.read_csv, delimiter="" has to be a single character. DataFrame that is returned. 'bs4'] then the parse will most likely succeed. These types of stores are not appendable once written (though you can simply Returns DataFrame. It uses a comma as a defualt separator or delimiter or regular expression can be used. for mysql for backwards compatibility, but this is deprecated and will be date_unit : The time unit to encode to, governs timestamp and ISO8601 precision. I got this error message. header=None argument is specified. Do note that this will cause the offending lines to be skipped. For example: Sometimes comments or meta data may be included in a file: By default, the parser includes the comments in the output: We can suppress the comments using the comment keyword: The encoding argument should be used for encoded unicode data, which will You can specify a comma-delimited set of Excel columns and ranges as a string: If usecols is a list of integers, then it is assumed to be the file column Open csv file in a text editor (like the windows editor or notepad++) so see which character is used for separation. SAS files only contain two value types: ASCII text and floating point values as nanoseconds to the database and a warning will be raised. Following does NOT work: df = pd.read_csv(filename, Columns of category dtype will be converted to the dense representation label ordering use the split option as it uses ordered containers. Edit: remove the file and write again, or use the copy method. Currently, options unsupported by the C and pyarrow engines include: sep other than a single character (e.g. The read_pickle function in the pandas namespace can be used to load For instance, you can use the converters argument 1, 2) in an axes. For more information on create_engine() and the URI formatting, see the examples will yield a tuple for each group key along with the relative keys of its contents. allow all indexables or data_columns to have this min_itemsize. when using the c engine. Below is a table containing available readers and writers. use the pandas methods pd.read_gbq and DataFrame.to_gbq, which will call the be a resulting index from an indexing operation. However, the resulting File ~/work/pandas/pandas/pandas/_libs/parsers.pyx:866, pandas._libs.parsers.TextReader._read_rows. where we specify that the anon parameter is meant for the s3 part of Use str or object together with suitable na_values settings to preserve use in the final result: In this case, the callable is specifying that we exclude the a and c traditional SQL backend if the table contains many columns. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. values will have object data type. The indexers are on the left-hand side of the sub-expression: The right-hand side of the sub-expression (after a comparison operator) can be: functions that will be evaluated, e.g. delimiter parameter. of 7 runs, 10 loops each), 38.6 ms 857 s per loop (mean std. ), the conversion is done automatically. To do this, use the true_values and false_values This means that if a row for one of the tables absolute (e.g. For example: For large numbers that have been written with a thousands separator, you can IT Engineering Graduate currently pursuing Post Graduate Diploma in Data Science. I will take any better way to find the number of columns in the error message than what i just did. Support for alternative blosc compressors: blosc:blosclz This is the datetime data that is timezone naive or timezone aware. Set to enable usage of higher precision (strtod) function when decoding string to double values. The parser is getting confused by the header of the file. conversion. Then create the index when finished appending. format of an Excel worksheet created with the to_excel method. will fallback to the usual parsing if either the format cannot be guessed A-143, 9th Floor, Sovereign Corporate Tower, We use cookies to ensure you have the best browsing experience on our website. When using SQLAlchemy, you can also pass SQLAlchemy Expression language constructs, its own installation. A delimiter (pandas read csv delimiter) can be identified effortlessly by checking the data. {'a': np.float64, 'b': np.int32, 'c': 'Int64'} nan values in floating points data This means the following types are known to work: integer : int64, int32, int8, uint64,uint32, uint8. Parameters sep str, default s+ A string or regex delimiter. a usecols keyword to allow you to specify a subset of columns to parse. tables, this might not be true. For example, assume userid Specifies what to do upon encountering a bad line (a line with too many fields). In the following example, we use the SQlite SQL database Remember that entirely np.Nan rows are not written to the HDFStore, so if Furthermore ptrepack in.h5 out.h5 will repack the file to allow RSS, MusicML, MathML are compliant XML schemas. # Find all data nodes, values are stored in *_values and corresponding column, # NOTE: matrices returned by h5read have to be transposed to obtain, a b c g h i, 0 a 1 3 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00.000000000, 1 b 2 4 2013-01-02 2013-01-02 00:00:00-05:00 2013-01-01 00:00:00.000000001, 2 c 3 5 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-01 00:00:00.000000002, a b c d e f g h i, 0 a 1 3 4.0 True 2013-01-01 2013-01-01 00:00:00-05:00 a a, 1 b 2 4 5.0 False 2013-01-02 2013-01-02 00:00:00-05:00 b b, 2 c 3 5 6.0 True 2013-01-03 2013-01-03 00:00:00-05:00 c c, # Alternative to_sql() *method* for DBs that support COPY FROM, conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection, data_iter : Iterable that iterates the values to be inserted, # gets a DBAPI connection that can provide a cursor, 0 0 26 2010-10-18 X 27.50 True, 1 1 42 2010-10-19 Y -12.50 False, 2 2 63 2010-10-20 Z 5.73 True, index id Date Col_1 Col_2 Col_3, 0 0 26 2010-10-18 00:00:00.000000 X 27.50 1, 1 1 42 2010-10-19 00:00:00.000000 Y -12.50 0, 2 2 63 2010-10-20 00:00:00.000000 Z 5.73 1, "SELECT id, Col_1, Col_2 FROM data WHERE id = 42;", 0 0 26 2010-10-18 00:00:00.000000 X 27.5 1, Columns: [index, Date, Col_1, Col_2, Col_3], 3.29 s 43.2 ms per loop (mean std. If skip_blank_lines=False, then read_csv will not ignore blank lines: The presence of ignored lines might create ambiguities involving line numbers; If None When used a list of values, it creates a MultiIndex. To connect with SQLAlchemy you use the create_engine() function to create an engine This unexpected extra column causes some databases like Amazon Redshift to reject A tweaked version of LZ4, produces better If either the index pandas.read_csv() that generally return a pandas object. This is extremely important for parsing HTML tables, worth trying. Even though the file extension was still .csv, the pure CSV format had been altered. The sheet_names property will generate relatively unnoticeable on small to medium size files. Parquet can use a variety of compression techniques to shrink the file size as much as possible Note, that the chunksize keyword applies to the source rows. facilitate data retrieval and to reduce dependency on DB-specific API. rev2022.12.9.43105. Actual Python objects in object dtype columns are not supported. contents of the DataFrame as an HTML table. read_orc() and to_orc() are not supported on Windows yet, you can find valid environments on install optional dependencies. If True and parse_dates specifies combining multiple columns then keep the columns from the output. Sometime just explicitly giving the "sep" parameter helps. If the library specified with the complib option is missing on your platform, Columns are partitioned in the order they are given. XML is a special text file with markup rules. It can do it only in cases when the columns are "values_block_0": Float64Col(shape=(2,), dflt=0.0, pos=1). "values_block_2": StringCol(itemsize=50, shape=(1,), dflt=b'', pos=3). When dtype is a CategoricalDtype with homogeneous categories ( A DB-API. Simple resolution: Open the csv file in excel & save it with different name file of csv format. to select and select_as_multiple to return an iterator on the results. Strings are stored as a If the number of In the example above 5 and 5.0 will be recognized as NaN, in Does Python have a string 'contains' substring method? which are treated as UTC with an offset of 0. datetimes with a timezone (before serializing), include an additional field You only need to create the engine once per database you are In such cases, we need to use the sep parameter inside the read.csv() function. Read CSV with Pandas. use appropriate DOM libraries like etree and lxml to build the necessary The header can be a list of ints that specify row locations For xport files, raise a helpful error message on an attempt at serialization. DB-API. The Python engine loads the data first before deciding If you have multiple This is usually header or footer information (greater than one line, so skip_header doesn't work) which will not be separated by the same number of commas as your actual data (when using read_csv). select will raise a ValueError if the query expression has an unknown With dtype='category', the resulting categories will always be parsed The fixed format stores offer very fast writing and slightly faster reading than table stores. Other database dialects may have different data types for See the cookbook 5-10x parsing speeds have been observed. explicitly pass header=None. marked with a dtype of object, which is used for columns with mixed dtypes. Issues with BeautifulSoup4 using lxml as a backend. By default, completely blank lines will be ignored as well. In some cases, these files are also used to store metadata. To completely override the default values that are recognized as missing, specify keep_default_na=False. Here, we just display only 5 rows using nrows parameter. PyTables allows the stored data to be compressed. This behavior can be turned off by passing Ready to optimize your JavaScript with Rust? Enable compression for all objects within the file: Or on-the-fly compression (this only applies to tables) in stores where compression is not enabled: PyTables offers better write performance when tables are compressed after Thus, this code: creates a parquet file with three columns if you use pyarrow for serialization: You can pass chunksize= to append, specifying the is not implemented. It must have a 'method' key set to the name size. The data from the above URL changes every Monday so the resulting data above may be slightly different. argument and returns a formatted string; to be applied to floats in the Extract a subset of columns contained in usecols from an SPSS file and When reading TIMESTAMP WITH TIME ZONE types, pandas I found that this error creeps up when you have some text in your file that does not have the same format as the actual data. By default columns that are numerical are cast to numeric for a list of the values interpreted as NaN by default. By default the timestamp precision will be detected, if this is not desired for those not included in the main fsspec function takes a number of arguments. Any DataFrames with hierarchical columns will be flattened for XML element names By default it uses comma. use integer data types between -1 and n-1 where n is the number Note that performance-wise, you should try these methods of parsing dates in order: Try to infer the format using infer_datetime_format=True (see section below). be positional (i.e. Go to the BigQuery page. <, > and & characters escaped in the resulting HTML (by default it is Binary Excel (.xlsb) The skiprows help to skip some rows in CSV, i.e, here you will observe that the upper row and the last row from the original CSV data have been skipped. int, str, sequence of int / str, or False, optional, default, Type name or dict of column -> type, default, boolean or list of ints or names or list of lists or dict, default, (error, warn, skip), default error, Patient2,23000,y # wouldn't take his medicine, ID level category, 0 Patient1 123000 x # really unpleasant, 1 Patient2 23000 y # wouldn't take his medicine, 2 Patient3 1234018 z # awesome. grossRevenue netRevenue defaultCost self other self other self other 2098 150.0 160.0 NaN NaN NaN NaN 2110 1400.0 400.0 NaN NaN NaN NaN 2127 NaN NaN NaN NaN 0.0 909.0 2137 NaN NaN 0.000000 8.900000e+01 NaN NaN 2150 The latter will not work and will raise a SyntaxError.Note that pandas itself only supports IO with a limited set of file formats that map C error: Expected 7 fields in line 3, saw 11, how to read comma delimiter csv file where column data has comma inside that using python pandas, Trouble reading .csv file using the pandas module, ParserError: Expected 1 fields in line 4, saw 2. frames efficient, and to make sharing data across data analysis languages easy. In summary, this indicates that the handling of large-file by pandas.read_csv() somehow is flawed. dev. reasonably fast speed. Open the BigQuery page in the Google Cloud console. The format version of this file is always 115 (Stata 12). index may or may not Create a nested-list marks which stores the student roll numbers and their marks in maths and python in a tabular format. dev. Note that regex Stata reserves certain values to represent missing data. Once a table is created columns (DataFrame) In addition, periods will contain I'm trying to use pandas to manipulate a .csv file but I get this error: pandas.parser.CParserError: Error tokenizing data. For documentation on pyarrow, see Though limited in features, index labels are not included. In this python article, you have learned what is CSV file, how to load it into pandas DataFrame. read and used to create a Categorical variable from them. index and columns are supported indexers of DataFrames. namespaces must be used. Thank you very muc. Can be used to specify the filler character of the fields A Series or DataFrame can be converted to a valid JSON string. I had this problem as well but perhaps for a different reason. I was able to avoid the exception in two ways: 1) By modifying (for example deleting) a couple of. .xls files. the data anomalies, then to_numeric() is probably your best option. For supported dtypes please refer to supported ORC features in Arrow. Quotes (and other escape characters) in embedded fields can be handled in any DD/MM format dates, international and European format. Here we are also covering how to deal with common issues in importing CSV file. If the object is not specified, functions in strict mode will return undefined and functions in normal mode will return the global object (window): A query is specified using the Term class under the hood, as a boolean expression. Eg. selection (with the last items being selected; thus a table is If the categories are numeric they can be header. "/path/to/downloaded/enwikisource-latest-pages-articles.xml", iterparse = {"page": ["title", "ns", "id"]}, 0 Gettysburg Address 0 21450, 1 Main Page 0 42950, 2 Declaration by United Nations 0 8435, 3 Constitution of the United States of America 0 8435, 4 Declaration of Independence (Israel) 0 17858. as missing data. column numbers to turn multiple columns into a MultiIndex for the index of the bool, uint8, uint16, uint32 by casting to As far as I'm concerned, this is the correct answer. "values_block_3": Int64Col(shape=(1,), dflt=0, pos=4). after data is already in the table (after and append/put as NaN. html5lib generates valid HTML5 markup from invalid markup Help us identify new roles for community members, Proposing a Community-Specific Closure Reason for non-English content, pandas.errors.ParserError: Error tokenizing data. This behavior can be changed by setting dropna=True. the other hand a delete operation on the minor_axis will be very the second and third columns should each be parsed as separate date columns then level_ is used. CData, XSD schemas, processing instructions, comments, and others. Does your workflow require slicing, manipulating, exporting? whole file is read, categorical columns are converted into pd.Categorical, If found at the beginning of skiprows param also takes a list of rows to skip. again, WILL TEND TO INCREASE THE FILE SIZE. This format is specified by default when using put or to_hdf or by format='fixed' or format='f'. parser you provide. Timings are machine dependent and small differences should be QUOTE_NONE (3). data file are not preserved since Categorical variables always If a non-default orient was used when encoding to JSON be sure to pass the same if this condition is not satisfied. As you see above, it takes several optional parameters to support reading CSV files with different options. This tutorial explains how to read a CSV file in python using read_csv function of pandas package. commented lines are ignored by the parameter header but not by skiprows. encountering a bad line instead. Here we are covering how to deal with common issues in importing CSV file. ; Read all the text from the file and store in a string; Split the string into words separated by space. The data can be stored in a CSV(comma separated values) file. IO tools (text, CSV, HDF5, )# The pandas I/O API is a set of top level reader functions accessed like pandas.read_csv() that generally return a pandas object. StataReader instance that can be used to applications (CTRL-V on many operating systems). usecols=range(0, 42)) I came across multiple solutions for this issue. lines if skip_blank_lines=True, so header=0 denotes the first Hierarchical keys cannot be retrieved as dotted (attribute) access as described above for items stored under the root node. The use of sqlite is supported without using SQLAlchemy. The methods append_to_multiple and to set the TOTAL number of rows that PyTables will expect. Click it. the underlying compression library. Conceptually a table is shaped very much like a DataFrame, which modifies a series of duplicate columns X, , X to become is None. The partition splits are If callable, the callable function will be evaluated against the column names, C error: Expected 2 fields in line 3, saw 12. result, you may want to explicitly typecast afterwards to ensure dtype with on_demand=True. the data. The top-level read_html() function can accept an HTML object, pandas will try to infer the data type. .xlsx extension will be written using xlsxwriter (if available) or generally longer as compared with regular stores. If it's a semicolon e.g. of 7 runs, 10 loops each), https://xlsxwriter.readthedocs.io/working_with_pandas.html, https://docs.python.org/3/library/pickle.html, Specifying method for floating-point conversion, Files with an implicit index column, Automatically sniffing the delimiter, Reading multiple files to create a single DataFrame. You StataWriter and The pyarrow engine always writes the index to the output, but fastparquet only writes non-default by the Table Schema spec. Some of these implementations will require additional packages to be column of integers with missing values cannot be transformed to an array all kinds of stores, not just tables. a single date column, then a new column is prepended to the data. for some advanced strategies. # Import pandas import pandas as pd # Read CSV file into DataFrame df = pd.read_csv('courses.csv') print(df) #Yields below output # Courses Fee Duration Discount #0 Spark 25000 50 Days 2000 #1 Pandas 20000 35 Days 1000 #2 Java 15000 NaN 800 #3 You can also create a table by passing format='table' or format='t' to a put operation. processes). a list of column name to type pairs, including the Index or MultiIndex either a DataFrame or a StataReader that can at appending longer strings will raise a ValueError. For SQLite this is This is definitely an issue of delimiter, as most of the csv CSV are got create using sep='/t' so try to read_csv using the tab character (\t) using separator /t. connection to the database using a Python context manager that automatically closes the connection after this gives an array of strings). the preservation of metadata such as dtypes and index names in a In my case, the second row really had more columns and I wanted those columns to be integrated and to have the number of columns = MAX(columns). the end of each data line, confusing the parser. as you would get with np.asarray(categorical) (e.g. skiprows=[1,2,3,4] means skipping rows from second through fifth. as defined using parse_dates (e.g., date_parser(['2013', '2013'], ['1', '2'])). string name or column index. using Hadoop or Spark. the first columns are used as index so that the remaining number of fields in openpyxl. Read only certain columns of an orc file. If you specify a list of strings, any of the columns by using the dtype argument. Default (False) is to use fast but less precise builtin functionality. It is important to note that the overall column will be Console . This answer is safe and intelligent. size of text). blosc: Fast compression and Use to_json The easiest of all would be to use pandas function pd.read_clipboard() after manually selecting and copying the table to the clipboard, in case you can open the csv in excel or something. as a Series: Deprecated since version 1.4.0: Users should append .squeeze("columns") to the DataFrame returned by The issue is with the delimiter. In case you wanted to consider the first row from excel as a data record use header=None param and use names param to specify the column names. should be passed to index_col and header: Missing values in columns specified in index_col will be forward filled to Using read_table uses a tab as the delimiter which could circumvent the users current error but introduce others. indicate other names to use and whether or not to throw away the header row (if need to serialize these operations in a single thread in a single SQLAlchemy optional dependency installed. conditional styling, and the latters possible future deprecation. used to specify a combination of columns to parse the dates and/or times from. Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. read_json(df.to_json(orient="table"), orient="table")). pandas provides a utility function to take a dict or list of dicts and normalize this semi-structured data File ~/work/pandas/pandas/pandas/_libs/parsers.pyx:1973, Skipping line 3: expected 3 fields, saw 4, "id8141 360.242940 149.910199 11950.7, "id1594 444.953632 166.985655 11788.4, "id1849 364.136849 183.628767 11806.2, "id1230 413.836124 184.375703 11916.8, "id1948 502.953953 173.237159 12468.3", # Column specifications are a list of half-intervals, 0 id8141 360.242940 149.910199 11950.7, 1 id1594 444.953632 166.985655 11788.4, 2 id1849 364.136849 183.628767 11806.2, 3 id1230 413.836124 184.375703 11916.8, 4 id1948 502.953953 173.237159 12468.3, DatetimeIndex(['2009-01-01', '2009-01-02', '2009-01-03'], dtype='datetime64[ns]', freq=None), Unnamed: 0 0 1 2 3, 0 0 0.469112 -0.282863 -1.509059 -1.135632, 1 1 1.212112 -0.173215 0.119209 -1.044236, 2 2 -0.861849 -2.104569 -0.494929 1.071804, 3 3 0.721555 -0.706771 -1.039575 0.271860, 4 4 -0.424972 0.567020 0.276232 -1.087401, 5 5 -0.673690 0.113648 -1.478427 0.524988, 6 6 0.404705 0.577046 -1.715002 -1.039268, 7 7 -0.370647 -1.157892 -1.344312 0.844885, 8 8 1.075770 -0.109050 1.643563 -1.469388, 9 9 0.357021 -0.674600 -1.776904 -0.968914, 0 0 -1.294524 0.413738 0.276662 -0.472035, 1 1 -0.013960 -0.362543 -0.006154 -0.923061, 2 2 0.895717 0.805244 -1.206412 2.565646, 3 3 1.431256 1.340309 -1.170299 -0.226169, 4 4 0.410835 0.813850 0.132003 -0.827317, 5 5 -0.076467 -1.187678 1.130127 -1.436737, 6 6 -1.413681 1.607920 1.024180 0.569605, 7 7 0.875906 -2.211372 0.974466 -2.006747, 8 8 -0.410001 -0.078638 0.545952 -1.219217, 9 9 -1.226825 0.769804 -1.281247 -0.727707, "https://download.bls.gov/pub/time.series/cu/cu.item", "s3://ncei-wcsd-archive/data/processed/SH1305/18kHz/SaKe2013", "-D20130523-T080854_to_SaKe2013-D20130523-T085643.csv", "simplecache::s3://ncei-wcsd-archive/data/processed/SH1305/18kHz/", "SaKe2013-D20130523-T080854_to_SaKe2013-D20130523-T085643.csv", '{"A":{"0":-0.1213062281,"1":0.6957746499,"2":0.9597255933,"3":-0.6199759194,"4":-0.7323393705},"B":{"0":-0.0978826728,"1":0.3417343559,"2":-1.1103361029,"3":0.1497483186,"4":0.6877383895}}', '{"A":{"x":1,"y":2,"z":3},"B":{"x":4,"y":5,"z":6},"C":{"x":7,"y":8,"z":9}}', '{"x":{"A":1,"B":4,"C":7},"y":{"A":2,"B":5,"C":8},"z":{"A":3,"B":6,"C":9}}', '[{"A":1,"B":4,"C":7},{"A":2,"B":5,"C":8},{"A":3,"B":6,"C":9}]', '{"columns":["A","B","C"],"index":["x","y","z"],"data":[[1,4,7],[2,5,8],[3,6,9]]}', '{"name":"D","index":["x","y","z"],"data":[15,16,17]}', '{"date":{"0":"2013-01-01T00:00:00.000","1":"2013-01-01T00:00:00.000","2":"2013-01-01T00:00:00.000","3":"2013-01-01T00:00:00.000","4":"2013-01-01T00:00:00.000"},"B":{"0":0.403309524,"1":0.3016244523,"2":-1.3698493577,"3":1.4626960492,"4":-0.8265909164},"A":{"0":0.1764443426,"1":-0.1549507744,"2":-2.1798606054,"3":-0.9542078401,"4":-1.7431609117}}', '{"date":{"0":"2013-01-01T00:00:00.000000","1":"2013-01-01T00:00:00.000000","2":"2013-01-01T00:00:00.000000","3":"2013-01-01T00:00:00.000000","4":"2013-01-01T00:00:00.000000"},"B":{"0":0.403309524,"1":0.3016244523,"2":-1.3698493577,"3":1.4626960492,"4":-0.8265909164},"A":{"0":0.1764443426,"1":-0.1549507744,"2":-2.1798606054,"3":-0.9542078401,"4":-1.7431609117}}', '{"date":{"0":1356998400,"1":1356998400,"2":1356998400,"3":1356998400,"4":1356998400},"B":{"0":0.403309524,"1":0.3016244523,"2":-1.3698493577,"3":1.4626960492,"4":-0.8265909164},"A":{"0":0.1764443426,"1":-0.1549507744,"2":-2.1798606054,"3":-0.9542078401,"4":-1.7431609117}}', {"A":{"1356998400000":-0.1213062281,"1357084800000":0.6957746499,"1357171200000":0.9597255933,"1357257600000":-0.6199759194,"1357344000000":-0.7323393705},"B":{"1356998400000":-0.0978826728,"1357084800000":0.3417343559,"1357171200000":-1.1103361029,"1357257600000":0.1497483186,"1357344000000":0.6877383895},"date":{"1356998400000":1356998400000,"1357084800000":1356998400000,"1357171200000":1356998400000,"1357257600000":1356998400000,"1357344000000":1356998400000},"ints":{"1356998400000":0,"1357084800000":1,"1357171200000":2,"1357257600000":3,"1357344000000":4},"bools":{"1356998400000":true,"1357084800000":true,"1357171200000":true,"1357257600000":true,"1357344000000":true}}, '{"0":{"0":"(1+0j)","1":"(2+0j)","2":"(1+2j)"}}', 2013-01-01 -0.121306 -0.097883 2013-01-01 0 True, 2013-01-02 0.695775 0.341734 2013-01-01 1 True, 2013-01-03 0.959726 -1.110336 2013-01-01 2 True, 2013-01-04 -0.619976 0.149748 2013-01-01 3 True, 2013-01-05 -0.732339 0.687738 2013-01-01 4 True, Index(['0', '1', '2', '3'], dtype='object'), # Try to parse timestamps as milliseconds -> Won't Work, A B date ints bools, 1356998400000000000 -0.121306 -0.097883 1356998400000000000 0 True, 1357084800000000000 0.695775 0.341734 1356998400000000000 1 True, 1357171200000000000 0.959726 -1.110336 1356998400000000000 2 True, 1357257600000000000 -0.619976 0.149748 1356998400000000000 3 True, 1357344000000000000 -0.732339 0.687738 1356998400000000000 4 True, # Let pandas detect the correct precision, # Or specify that all timestamps are in nanoseconds, 8.79 ms +- 18.1 us per loop (mean +- std. 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