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All Pandas Data Types


All Pandas Data Types

By : Smaz Control
Date : November 21 2020, 07:35 AM
fixed the issue. Will look into that further Pandas uses datetime64, timedelta64, int64, float64, bool and object (this includes strings and any other custom objects). You can use other types such as float32, and pandas will try to maintain it, but some operations will implicitly cast to e.g., float64.
code :


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pandas string data types

pandas string data types


By : rom m
Date : March 29 2020, 07:55 AM
hope this fix your issue As Jeff indicated, my syntax was bad. The names and types have to be zipped into a dic style list of relationships. The code below works, but note that you can't dtype a string width; you can only define it as an object.
code :
import pandas as pd
import io

csv = """foo,1234567,a,1
foo,2345678,b,3
bar,3456789,b,5
"""

df = pd.read_csv(io.StringIO(csv),
        names = ["fb", "num", "ab", "x"], 
        dtype = {"fb" : object, "num" : np.int64, "ab" : object, "x" : np.int8})
print(df)
Changing Data Types in Pandas Data Frame

Changing Data Types in Pandas Data Frame


By : Evgeniy Makhrov
Date : March 29 2020, 07:55 AM
Hope this helps Below is the last part of my selenium web scraper that loops through the different tabs of this website page, selects the "export data" button, downloads the data, adds a "yearid" column, then loads the data into a MySQL table. , Demo:
parse URL into a DF:
code :
In [263]: df = pd.read_html(url, header=1)[11]
In [264]: df[df.columns[df.columns.str.contains('%')]]
Out[264]:
       BB%      K%
0   18.5 %  19.2 %
1   12.8 %  11.5 %
2   11.0 %  13.1 %
3    8.7 %  18.3 %
4   13.5 %  16.0 %
..     ...     ...
26   7.0 %  20.2 %
27  13.5 %  12.5 %
28   9.4 %  16.1 %
29   8.6 %  21.5 %
30     NaN     NaN

[31 rows x 2 columns]
In [265]: df[df.columns[df.columns.str.contains('%')]] = \
              (df.filter(regex='%')
                 .apply(lambda x: pd.to_numeric(x.str.replace(r'[\s%]',''), 
                                                errors='coerce')))
In [266]: df[df.columns[df.columns.str.contains('%')]]
Out[266]:
     BB%    K%
0   18.5  19.2
1   12.8  11.5
2   11.0  13.1
3    8.7  18.3
4   13.5  16.0
..   ...   ...
26   7.0  20.2
27  13.5  12.5
28   9.4  16.1
29   8.6  21.5
30   NaN   NaN

[31 rows x 2 columns]

In [267]: df[df.columns[df.columns.str.contains('%')]].dtypes
Out[267]:
BB%    float64
K%     float64
dtype: object
sklearn: Pandas Dataframe vs Numpy ndarray - Which is more efficient to hold a [600k * 1k] data of different data types

sklearn: Pandas Dataframe vs Numpy ndarray - Which is more efficient to hold a [600k * 1k] data of different data types


By : user1491873
Date : March 29 2020, 07:55 AM
With these it helps ndarray will be more efficient for the amount of information that you have provided, reason is obvious as pandas are designed for diverse purposes and performance isn't the most important consideration with respect to its flexibility and user friendliness, you can look at this example for more details
How can I make a dictionary from a pandas data frame where the values are data types?

How can I make a dictionary from a pandas data frame where the values are data types?


By : Rajesh Sahoo
Date : March 29 2020, 07:55 AM
Hope this helps There are two ways to do it that I can think of.
One is to use a dict mapping, as follows:
code :
dtype_mapping = {'np.uint8': np.uint8,
                 'np.uint16': np.uint16,
                 ...all your dtypes here...
                 'object': 'np.object'}

dtypes = [dtype_mapping[dtype] for dtype in Lookup['Type']]

dic = dict(zip(Lookup['Variable'].tolist(), dtypes)
dtypes = [eval(dtype) for dtype in Lookup['Type']]

dic = dict(zip(Lookup['Variable'].tolist(), dtypes))
Pandas - enforcing automatic data types in pandas.DataFrame() with data having lots of missing values

Pandas - enforcing automatic data types in pandas.DataFrame() with data having lots of missing values


By : user3207304
Date : March 29 2020, 07:55 AM
this will help You can convert Time column to index by DataFrame.set_index, then if possible convert to timedeltas and all columns convert to numeric by to_numeric with errors='coerce' for convert non numeric values to NaNs:
code :
df = df.set_index('Time')
df.index = pd.to_timedelta(df.index)
df = df.apply(pd.to_numeric, errors='coerce')
print (df.dtypes)
Col2     float64
Col3     float64
Col4     float64
Col5     float64
Col6     float64
Col7     float64
Col8     float64
Col9     float64
Col10    float64
Col11    float64
Col12    float64
Col13    float64
dtype: object
colVals = [['05:17:55.703', '', '', '', '', '', '21', '', '3', '89', '891', '11', ''], 
           ['05:17:55.703', '', '', '', '', '', '21', '', '3', '217', '891', '12', ''], 
           ['05:17:55.703', '', '', '', '', '', '21', '', '3', '217', '891', '13', '']]
colNames = ["Time","Col2","Col3","Col4","Col5","Col6","Col7","Col8","Col9",
                   "Col10","Col11","Col12","Col13"]
df = pd.DataFrame(colVals, columns=colNames)

df = df.set_index('Time')
df.index = pd.to_timedelta(df.index)

def func(x):
    try:
        return x.astype(float).astype(int)
    except:
        return pd.to_numeric(x, errors='coerce')

df = df.apply(func)
print (df.dtypes)
Col2     float64
Col3     float64
Col4     float64
Col5     float64
Col6     float64
Col7       int32
Col8     float64
Col9       int32
Col10      int32
Col11      int32
Col12      int32
Col13    float64
dtype: object
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