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- pd.Series()
- pd.DataFrame()
- pd.read_pickle()
- pd.read_table()
- pd.read_csv()
- pd.read_fwf()
- pd.read_clipboard()
- pd.read_excel()
- pd.ExcelFile.parse()
- pd.read_json()
- pd.json_normalize()
- pd.read_html()
- pd.read_xml()
- pd.read_hdf()
- pd.read_feather()
- pd.read_parquet()
- pd.read_orc()
- pd.read_sql_table()
- pd.read_sql_query()
- pd.read_sql()
Creation | pd.Series()
Method:
pd.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=<no_default>)
The constructor creates a one-dimensional labeled array, capable of holding any data type (integers, strings, floats, Python objects, etc.).
Returns:
pd.core.series.Series
Parameters:
data: (array-like, iterable, dict, scalar), Optional-
index: str, Optional-
A sequence of labels for the data. If None, a default integer index (0, 1, 2, ...) is assigned.
import pandas as pd
# Creating a Series
s = pd.Series(data=[10, 20, 30], index=['x', 'y', 'z'], name='Numbers')
print(s)
'''
Output:
x 10
y 20
z 30
Name: Numbers, dtype: int64
'''If index is provided, it must always match with the length of the data.
dtype: data-type, Optional-
Specifies the data-type of the Series. If not provided, it’s inferred from the input.
import pandas as pd
# Creating a Series
s = pd.Series(data=[10, 20, 30], index=['x', 'y', 'z'], dtype='int8', name='Numbers')
print(s)
'''
Output:
x 10
y 20
z 30
Name: Numbers, dtype: int8
'''Values: +
Integer Types
int8: 8-bit signed integer (range: -128 to 127).
int16: 16-bit signed integer (range: -32,768 to 32,767).
int32: 32-bit signed integer (range: -2,147,483,648 to 2,147,483,647).
int64: 64-bit signed integer (large integer range).
uint8: 8-bit unsigned integer (range: 0 to 255).
uint16: 16-bit unsigned integer (range: 0 to 65,535).
uint32: 32-bit unsigned integer (range: 0 to 4,294,967,295).
uint64: 64-bit unsigned integer (large positive integer range).
Floating Types
float16: Half precision floating-point (16-bit, for low-precision computations).
float32: Single precision floating-point (32-bit).
float64: Double precision floating-point (64-bit, the default float in NumPy).
float128: Extended precision floating-point (128-bit, availability depends on system).
Comples Number Types
complex64: Complex number represented by two 32-bit floats (for real and imaginary parts).
complex128: Complex number represented by two 64-bit floats (default complex dtype).
complex256: Complex number represented by two 128-bit floats (system-dependent).
Boolean Type
bool: Boolean type, can be either True or False (stored as 1-bit but takes up a full byte).
String Types
str: Fixed-length Unicode string, specified by S + length (e.g., S10 for a 10-character string).
unicode: Fixed-length Unicode string with support for multiple characters (uses U).
Object Type
object: Allows storing any Python object, including mixed types, strings, or other arrays. Useful for heterogeneous data but slower than native NumPy types.
Datetime & Timedelta Types
datetime64: Stores dates and times with varying precisions (e.g., Y, M, D, h, m, s, ms, us, ns, ps, fs, as). Example: datetime64('2003-10-02')
timedelta64: Represents time durations with units (same units as datetime64).
name: str, Optional-
copy: (True or False) Optional-
If True, a copy of the input data is created. This is useful if you want to ensure the original data remains unchanged.
copy = False (default) +
Changes to the new array will also affect the original array.
import pandas as pd
import numpy as np
# Original data as a NumPy array
original_data = np.array([1, 2, 3])
# Create a Series using the NumPy array
s = pd.Series(data=original_data, copy=False)
# Modify the original data (this will affect the Series)
original_data[0] = 100
# Print the Series
print(s)
'''
Output:
0 100
1 2
2 3
dtype: int64
'''copy = True +
A new Series is created, and the data is copied.
import pandas as pd
import numpy as np
# Original data as a NumPy array
original_data = np.array([1, 2, 3])
# Create a Series using the NumPy array
s = pd.Series(data=original_data, copy=True)
# Modify the original data (this will *not* affect the Series)
original_data[0] = 100
# Print the Series
print(s)
'''
Output:
0 1
1 2
2 3
dtype: int64
'''copy is handy only when the data is of the type np.ndarray.
Modification to the original_array or new_array will affect both the arrays.
Refer to numpy documentation.
fastpath: <no_default>, Optional-
An internal parameter used to optimize Series creation.